{ "metadata": { "name": "", "signature": "sha256:2cdd21435df51ddaf83453e30b87a17c30647a09506eb40337a4f13bef98aa72" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Baby names iPython notebooks #\n", "\n", " * By David Taylor, [www.prooffreader.com](http://www.prooffreader.com)\n", " * using data from United States Social Security Administration\n", " * I am making this public to give a head start to those who want to explore this dataset, so they don't have to download and format the data and the python objects used to do preliminary analysis. Please let me know if you find this helpful!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Printing graphs of names that match a list of names #" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Lists of names by origin #\n", "\n", "Dict of names and origins is from nameberry.com; it is closed-source, so will not be included in the git. Only metadata will be shown." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pprint\n", "import operator\n", "import json\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn\n", "import time\n", "import os\n", "import math" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "# load dicts\n", "origins = json.loads(open('private/name_origins.json', 'r').read())\n", "origins_counts = json.loads(open('private/name_origins_counts.json', 'r').read())\n", "import pprint\n", "pprint.pprint(sorted(origins_counts.items(), key=operator.itemgetter(1)))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[(u'Todas', 1),\n", " (u'Austrian', 1),\n", " (u'Bulgarian', 1),\n", " (u'Fijian', 1),\n", " (u'Celtic', 1),\n", " (u'Filipino', 1),\n", " (u'Serbian', 1),\n", " (u'Afghani', 1),\n", " (u'Italin', 1),\n", " (u'Old English Male', 1),\n", " (u'Sumerian', 1),\n", " (u'Singhalese', 1),\n", " (u'Breton', 1),\n", " (u'Swahili', 1),\n", " (u'Middle English', 1),\n", " (u'Samoan', 1),\n", " (u'Eskimo', 1),\n", " (u'SAm. Indian', 1),\n", " (u'Phrygian', 1),\n", " (u'Etruscan', 1),\n", " (u'Old Enligh', 1),\n", " (u'Ukranian', 1),\n", " (u'Kurdish', 1),\n", " (u'Bohemian', 1),\n", " (u'Khurdish', 1),\n", " (u'Old Danish', 2),\n", " (u'Incan', 2),\n", " (u'Assyrian', 2),\n", " (u'Tibetan', 2),\n", " (u'Flemish', 2),\n", " (u'Icelandic', 2),\n", " (u'Pakistani', 2),\n", " (u'Malaysian', 2),\n", " (u'Babylonian', 2),\n", " (u'Cornish', 3),\n", " (u'Syrian', 3),\n", " (u'Phoenician', 3),\n", " (u'Estonian', 4),\n", " (u'Norwegian', 4),\n", " (u'Invented', 4),\n", " (u'Danish', 4),\n", " (u'Swedish', 5),\n", " (u'Latvian', 5),\n", " (u'Spanish:Basque', 5),\n", " (u'Irish', 5),\n", " (u'Yiddish', 6),\n", " (u'East Indian', 6),\n", " (u'Lithuanian', 6),\n", " (u'Punjabi', 8),\n", " (u'Burmese', 8),\n", " (u'Hindi', 8),\n", " (u'Armenian', 9),\n", " (u'Israeli', 9),\n", " (u'Literary', 9),\n", " (u'Indonesian', 9),\n", " (u'Portuguese', 11),\n", " (u'Aramaic', 12),\n", " (u'Basque', 13),\n", " (u'Russian', 15),\n", " (u'French', 16),\n", " (u'Thai', 18),\n", " (u'Dutch', 21),\n", " (u'Finnish', 23),\n", " (u'English', 27),\n", " (u'Maori', 27),\n", " (u'Old Norse', 28),\n", " (u'Cambodian', 30),\n", " (u'Hungarian', 38),\n", " (u'Gypsy', 40),\n", " (u'Persian', 41),\n", " (u'German', 43),\n", " (u'Sanskrit', 62),\n", " (u'Korean', 72),\n", " (u'Turkish', 78),\n", " (u'Italian', 85),\n", " (u'Polish', 87),\n", " (u'Slavic', 99),\n", " (u'Czech', 115),\n", " (u'Vietnamese', 122),\n", " (u'Scots Gaelic', 140),\n", " (u'Chinese', 141),\n", " (u'Spanish', 162),\n", " (u'Tongan', 167),\n", " (u'Polynesian', 213),\n", " (u'Norse', 234),\n", " (u'Welsh', 252),\n", " (u'Old French', 285),\n", " (u'Australian Aboriginal', 301),\n", " (u'Old German', 344),\n", " (u'Japanese', 372),\n", " (u'Hawaiian', 393),\n", " (u'Latin', 545),\n", " (u'Irish Gaelic', 547),\n", " (u'Greek', 600),\n", " (u'Arabic', 680),\n", " (u'Hindu', 762),\n", " (u'African', 792),\n", " (u'Native North American', 850),\n", " (u'Hebrew', 1105),\n", " (u'Old English', 1209)]\n" ] } ], "prompt_number": 5 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "First pass with raw list" ] }, { "cell_type": "code", "collapsed": false, "input": [ "### user defined variables; if this were a function, they'd be arguments\n", "keyname = 'Australian Aboriginal'\n", "keys = ['Australian Aboriginal']\n", "dictname = origins\n", "top_cutoff = 20\n", "###\n", "\n", "for key in keys:\n", " assert key in dictname.keys()\n", "\n", "totals_title = \"Total {0} names in U.S. Social Security database, 1940-2013.\".format(keyname)\n", "top_boys_title = \"Top {0} {1} boys' names from U.S. Social Security database, 1940-2013\".format(top_cutoff, keyname)\n", "top_girls_title = \"Top {0} {1} girls' names from U.S. Social Security database, 1940-2013\".format(top_cutoff, keyname)\n", "last_year = 2013 #change this when Social Security database is updated\n", "save_path = \"user_charts\" # files created by this notebook will be saved in this directory\n", "if not os.path.isdir(save_path): # creates path if it does not exist\n", " os.makedirs(save_path)\n", "\n", "print 'This is standard output from download_and_process.py'\n", "%run download_and_process.py\n", "\n", "# use 1940+ only\n", "yob = yob1940.copy()\n", "names = names1940.copy()\n", "years = years1940.copy()\n", "\n", "all_listed = []\n", "for key in keys:\n", " all_listed += dictname[key]\n", " \n", "\n", "print '--------------------\\nFirst 80 characters of list:'\n", "all_listed_set = set(all_listed) # to remove duplicates\n", "all_listed = list(all_listed)\n", "print \"all_listed: list of length\", len(all_listed)\n", "\n", "# reduce names dataframe to those matching list\n", "print '--------------------\\nDataframe names filtered to those that match list'\n", "print \"%d records to begin.\" % (len(names))\n", "names_listed = names[names.name.isin(all_listed)].copy()\n", "names_listed.sort('pct_max', ascending=False, inplace=True)\n", "print \"%d records remaining.\" % (len(names_listed))\n", "listed_in_df = list(names_listed.name)\n", "print names_listed.head(10)\n", "listed_m = list(names[(names.sex == 'M') & (names.name.isin(listed_in_df))]['name'])\n", "listed_f = list(names[(names.sex == 'F') & (names.name.isin(listed_in_df))]['name'])\n", "\n", "#reduce yob dataframe to those matching list\n", "print '--------------------\\nDataframe yob filtered to those that match list (count only)'\n", "print \"%d records to begin.\" % (len(yob))\n", "yob_listed = yob[yob.name.isin(listed_in_df)].copy()\n", "yob_listed.sort(['year', 'sex', 'name'], ascending=False, inplace=True)\n", "print \"%d records remaining.\" % (len(yob_listed))\n", "\n", "# m and f totals\n", "yob_listed_f_agg = pd.DataFrame(yob_listed[yob_listed.sex == 'F'].groupby('year').sum())[['births', 'pct']]\n", "yob_listed_m_agg = pd.DataFrame(yob_listed[yob_listed.sex == 'M'].groupby('year').sum())[['births', 'pct']]\n", "print '--------------------\\nHead of total matching list per year, female'\n", "print yob_listed_f_agg.head()\n", "\n", "# print chart of m and f totals\n", "print '\\n'\n", "\n", "# function to determine a nice y-axis limit a little above the maximum value\n", "# rounds maximum y up to second-most-significant digit\n", "def determine_y_limit(x): \n", " significance = int(math.floor((math.log10(x))))\n", " val = math.floor(x / (10 ** (significance - 1))) + 1\n", " val = val * (10 ** (significance - 1))\n", " return val\n", "\n", "#data\n", "xf = list(yob_listed_f_agg.index)\n", "xm = list(yob_listed_m_agg.index)\n", "\n", "plt.figure(figsize=(16,9))\n", "plt.plot(xf, list(yob_listed_f_agg.pct), color=\"red\")\n", "plt.plot(xm, list(yob_listed_m_agg.pct), color=\"blue\")\n", "\n", "plt.ylim(0, determine_y_limit(max(list(yob_listed_f_agg.pct)\n", " +list(yob_listed_m_agg.pct))))\n", "plt.xlim(1940, 2013)\n", "\n", "plt.title(totals_title, fontsize = 20)\n", "plt.xlabel(\"Year\", fontsize = 14)\n", "plt.ylabel(\"% of total births of that sex\", fontsize = 14)\n", "\n", "plt.show()\n", "\n", "#function to make dataframe for top names\n", "\n", "def top_df(yobdf, names, sexes):\n", " \"\"\" yobdf = dataframe derived from yob; normally it would just be yob itself.\n", " names = list of names\n", " sexes = list of length 1 for all the same sex, or same length as names. 'F' and 'M' allowed\n", " \"\"\"\n", "\n", " df_chart = yobdf.copy()\n", " assert len(sexes) == 1 or len(names) == len(sexes)\n", " if len(sexes) == 1:\n", " sexes = sexes * len(names)\n", "\n", " df_chart = df_chart[df_chart['name'].isin(names)] \n", "\n", " df_chart['temp'] = 0\n", " for row in range(len(df_chart)):\n", " for pos in range(len(names)):\n", " if df_chart.name.iloc[row] == names[pos] and df_chart.sex.iloc[row] == sexes[pos]:\n", " df_chart.temp.iloc[row] = 1\n", " df_chart = df_chart[df_chart.temp == 1]\n", "\n", " print \"Tail of dataframe:\"\n", " print df_chart.tail()\n", "\n", " output_df = pd.DataFrame(pd.pivot_table(df_chart, values='pct', index = 'year', columns=['name', 'sex']))\n", "\n", " col = output_df.columns[0]\n", "\n", " for yr in range(1940, last_year + 1): #inserts missing years\n", " if yr not in output_df.index:\n", " #output_df[col][yr] = 0.0\n", " output_df = output_df.append(pd.DataFrame(index=[yr], columns=[col], data=[0.0]))\n", "\n", " output_df = output_df.fillna(0)\n", " \n", " return output_df\n", "\n", "listed_top_m = top_df(yob, listed_m[:top_cutoff], ['M'])\n", "listed_top_f = top_df(yob, listed_f[:top_cutoff], ['F'])\n", "\n", "#a single function to make the four different kinds of charts\n", "\n", "def make_chart(df, form='line', title='', colors= [], smoothing=0, \\\n", " groupedlist = [], baseline='sym', png_name=''):\n", " \n", " dataframe = df.copy()\n", " \n", " startyear = min(list(dataframe.index))\n", " endyear = max(list(dataframe.index))\n", " yearstr = '%d-%d' % (startyear, endyear)\n", " \n", " legend_size = 0.01\n", " \n", " has_male = False\n", " has_female = False\n", " has_both = False\n", " max_y = 0\n", " for name, sex in dataframe.columns:\n", " max_y = max(max_y, dataframe[(name, sex)].max())\n", " final_name = name\n", " if sex == 'M': has_male = True\n", " if sex == 'F': has_female = True\n", " if smoothing > 0:\n", " newvalues = []\n", " for row in range(len(dataframe)):\n", " start = max(0, row - smoothing)\n", " end = min(len(dataframe) - 1, row + smoothing)\n", " newvalues.append(dataframe[(name, sex)].iloc[start:end].mean())\n", " for row in range(len(dataframe)):\n", " dataframe[(name, sex)].iloc[row] = newvalues[row]\n", " if has_male and has_female:\n", " y_text = \"% of births of indicated sex\"\n", " has_both = True\n", " elif has_male:\n", " y_text = \"Percent of male births\"\n", " else:\n", " y_text = \"Percent of female births\"\n", " \n", " num_series = len(dataframe.columns)\n", " \n", " if colors == []:\n", " colors = ['#BB2114', '#0C5966', '#BA7814', '#4459AB', '#6B3838', \n", " '#B8327B', '#2B947F', '#0D83B5', '#684287', '#8C962C', \n", " '#92289E', '#242D7D']\n", " # my own list of dark contrasting colors\n", " num_colors = len(colors)\n", " \n", " if num_series > num_colors:\n", " print \"Warning: colors will be repeated.\"\n", " \n", " if title == '':\n", " if num_series == 1:\n", " title = \"Popularity of baby name %s in U.S., %s\" % (final_name, yearstr)\n", " else:\n", " title = \"Popularity of baby names in U.S., %s\" % (yearstr)\n", " \n", " x_values = range(startyear, endyear + 1)\n", " y_zeroes = [0] * (endyear - startyear)\n", " \n", " if form == 'line':\n", " fig, ax = plt.subplots(num=None, figsize=(16, 9), dpi=300, facecolor='w', edgecolor='w')\n", " counter = 0\n", " for name, sex in dataframe.columns:\n", " color = colors[counter % num_colors]\n", " counter += 1\n", " if has_both:\n", " label = \"%s (%s)\" % (name, sex)\n", " else:\n", " label = name\n", " ax.plot(x_values, dataframe[(name, sex)], label=label, color=color, linewidth = 3)\n", " ax.set_ylim(0,determine_y_limit(max_y)) \n", " ax.set_xlim(startyear, endyear)\n", " ax.set_ylabel(y_text, size = 13)\n", " ax.set_title(title, size = 18)\n", " box = ax.get_position()\n", " ax.set_position([box.x0, box.y0 + box.height * legend_size,\n", " box.width, box.height * (1 - legend_size)])\n", " legend_cols = min(5, num_series)\n", " ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=legend_cols)\n", "\n", " if form == 'subplots_auto':\n", " counter = 0\n", " fig, axes = plt.subplots(num_series, 1, figsize=(12, 3.5*num_series))\n", " print 'Maximum alpha: %d percent' % (determine_y_limit(max_y))\n", " for name, sex in dataframe.columns:\n", " if sex=='M':\n", " sex_label = 'male'\n", " else:\n", " sex_label = 'female'\n", " label = \"Percent of %s births for %s\" % (sex_label, name)\n", " current_ymax = dataframe[(name, sex)].max()\n", " tint = 1.0 * current_ymax / determine_y_limit(max_y)\n", " axes[counter].plot(x_values, dataframe[(name, sex)], color='k')\n", " axes[counter].set_ylim(0,determine_y_limit(current_ymax))\n", " axes[counter].set_xlim(startyear, endyear)\n", " axes[counter].fill_between(x_values, dataframe[(name, sex)], color=colors[0], alpha=tint, interpolate=True)\n", "\n", " axes[counter].set_ylabel(label, size=11)\n", " plt.subplots_adjust(hspace=0.1)\n", " counter += 1\n", " \n", " if form == 'subplots_same':\n", " counter = 0\n", " fig, axes = plt.subplots(num_series, 1, figsize=(12, 3.5*num_series))\n", " print 'Maximum y axis: %d percent' % (determine_y_limit(max_y))\n", " for name, sex in dataframe.columns:\n", " if sex=='M':\n", " sex_label = 'male'\n", " else:\n", " sex_label = 'female'\n", " label = \"Percent of %s births for %s\" % (sex_label, name)\n", " axes[counter].plot(x_values, dataframe[(name, sex)], color='k')\n", " axes[counter].set_ylim(0,determine_y_limit(max_y))\n", " axes[counter].set_xlim(startyear, endyear)\n", " axes[counter].fill_between(x_values, dataframe[(name, sex)], color=colors[1], alpha=1, interpolate=True)\n", " axes[counter].set_ylabel(label, size=11)\n", " plt.subplots_adjust(hspace=0.1)\n", " counter += 1\n", " \n", " if form == 'stream':\n", " plt.figure(num=None, figsize=(20,10), dpi=150, facecolor='w', edgecolor='k')\n", " plt.title(title, size=17) \n", " plt.xlim(startyear, endyear)\n", " \n", " if has_both:\n", " yaxtext = 'Percent of births of indicated sex (scale: '\n", " elif has_male:\n", " yaxtext = 'Percent of male births (scale: '\n", " else:\n", " yaxtext = 'Percent of female births (scale: '\n", " \n", " scale = str(determine_y_limit(max_y)) + ')'\n", " yaxtext += scale\n", " plt.ylabel(yaxtext, size=13)\n", " polys = plt.stackplot(x_values, *[dataframe[(name, sex)] for name, sex in dataframe.columns], \n", " colors=colors, baseline=baseline)\n", " legendProxies = []\n", " for poly in polys:\n", " legendProxies.append(plt.Rectangle((0, 0), 1, 1, fc=poly.get_facecolor()[0]))\n", " namelist = []\n", " for name, sex in dataframe.columns:\n", " if has_both:\n", " namelist.append('%s (%s)' % (name, sex))\n", " else:\n", " namelist.append(name)\n", " plt.legend(legendProxies, namelist, loc=3, ncol=2)\n", " \n", " plt.tick_params(\\\n", " axis='y', \n", " which='both', # major and minor ticks \n", " left='off', \n", " right='off', \n", " labelleft='off')\n", " \n", " plt.show() \n", " if png_name != '':\n", " filename = save_path + \"/\" + png_name + \".png\"\n", " plt.savefig(filename)\n", " plt.close()\n", "\n", "# line charts\n", "\n", "make_chart(df=listed_top_m,\n", " form='line', # line , subplots_auto , subplots_same , stream\n", " title=top_boys_title,\n", " colors= [],\n", " smoothing=0,\n", " baseline='zero', # zero , sym , wiggle , weighted_wiggle\n", " )\n", "\n", "make_chart(df=listed_top_f,\n", " form='line', # line , subplots_auto , subplots_same , stream\n", " title=top_girls_title,\n", " colors= [],\n", " smoothing=0,\n", " baseline='zero', # zero , sym , wiggle , weighted_wiggle\n", " )\n", "\n", "names_listed.reset_index(drop = True, inplace = True)\n", "names_listed.head()\n", "names_listed.to_csv('lists/names_matching_'+keyname+'.csv')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "This is standard output from download_and_process.py\n", "Data already downloaded.\n", "Data already extracted.\n", "Reading from pickle.\n", "Tail of dataframe 'yob':" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " name sex births year pct ranked\n", "1792086 Zyhier M 5 2013 0.000267 12995\n", "1792087 Zylar M 5 2013 0.000267 12995\n", "1792088 Zymari M 5 2013 0.000267 12995\n", "1792089 Zymeer M 5 2013 0.000267 12995\n", "1792090 Zyree M 5 2013 0.000267 12995\n", "\n", "Tail of dataframe 'names':\n", " name sex year_count year_min year_max pct_sum pct_max\n", "102685 Gross M 1 1925 1925 0.000538 0.000538\n", "102686 Elik M 1 2012 2012 0.000318 0.000318\n", "102687 Patrickjoseph M 1 1998 1998 0.000262 0.000262\n", "102688 Southern M 1 1923 1923 0.000547 0.000547\n", "102689 Jeon M 1 1999 1999 0.000261 0.000261\n", "\n", "Tail of dataframe 'years':\n", " year births_f births_m births_t new_names unique_names_x sexratio \\\n", "68 2008 1886765 2035811 3922576 2046 32483 107.899553 \n", "69 2009 1832276 1978582 3810858 1789 32210 107.984932 \n", "70 2010 1771846 1912915 3684761 1635 31593 107.961696 \n", "71 2011 1752198 1891800 3643998 1539 31412 107.967250 \n", "72 2012 1751866 1886972 3638838 1531 31212 107.712120 \n", "\n", " unique_names_y unique_names \n", "68 32483 32483 \n", "69 32210 32210 \n", "70 31593 31593 \n", "71 31412 31412 \n", "72 31212 31212 \n", "Tail of dataframe 'yob1940':\n", " name sex births year pct ranked\n", "1792086 Zyhier M 5 2013 0.000267 12995\n", "1792087 Zylar M 5 2013 0.000267 12995\n", "1792088 Zymari M 5 2013 0.000267 12995\n", "1792089 Zymeer M 5 2013 0.000267 12995\n", "1792090 Zyree M 5 2013 0.000267 12995\n", "Tail of dataframe 'names1940':\n", " name sex year_count year_min year_max pct_sum pct_max\n", "96949 Nyah M 1 2001 2001 0.000258 0.000258\n", "96950 Dajan M 1 2002 2002 0.000309 0.000309\n", "96951 Maung M 1 2009 2009 0.000253 0.000253\n", "96952 Charger M 1 2013 2013 0.000321 0.000321\n", "96953 Chrystal M 1 1987 1987 0.000268 0.000268\n" ] }, { "ename": "ValueError", "evalue": "Length mismatch: Expected axis has 8 elements, new values have 7 elements", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mC:\\Users\\David\\Documents\\Dropbox\\IPython_Synced\\GitHub\\Baby_names_US_IPython\\download_and_process.py\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 321\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 322\u001b[0m \u001b[0myears1940\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0myears1940\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'year'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'births_f'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'births_m'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'births_t'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'new_names'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'unique_names_x'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sexratio'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 323\u001b[1;33m \u001b[0myears1940\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'year'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'births_f'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'births_m'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'births_t'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'new_names'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'unique_names'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'sexratio'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 324\u001b[0m \u001b[1;31m# above lines correct outer merge problem\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 325\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[1;34m\"Tail of dataframe 'years1940':\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\David\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m 1847\u001b[0m This allows simpler access to columns for interactive use.\"\"\"\n\u001b[0;32m 1848\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_internal_names_set\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1849\u001b[1;33m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1850\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1851\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\David\\Anaconda\\lib\\site-packages\\pandas\\lib.pyd\u001b[0m in \u001b[0;36mpandas.lib.AxisProperty.__set__ (pandas\\lib.c:38491)\u001b[1;34m()\u001b[0m\n", "\u001b[1;32mC:\\Users\\David\\Anaconda\\lib\\site-packages\\pandas\\core\\generic.pyc\u001b[0m in \u001b[0;36m_set_axis\u001b[1;34m(self, axis, labels)\u001b[0m\n\u001b[0;32m 398\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 399\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_set_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 400\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 401\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_clear_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32mC:\\Users\\David\\Anaconda\\lib\\site-packages\\pandas\\core\\internals.pyc\u001b[0m in \u001b[0;36mset_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m 1963\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mnew_len\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mold_len\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1964\u001b[0m raise ValueError('Length mismatch: Expected axis has %d elements, '\n\u001b[1;32m-> 1965\u001b[1;33m 'new values have %d elements' % (old_len, new_len))\n\u001b[0m\u001b[0;32m 1966\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1967\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_labels\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mValueError\u001b[0m: Length mismatch: Expected axis has 8 elements, new values have 7 elements" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "--------------------\n", "First 80 characters of list:\n", "all_listed: list of length 491\n", "--------------------\n", "Dataframe names filtered to those that match list\n", "96954 records to begin.\n", "188 records remaining.\n", " name sex year_count year_min year_max pct_sum pct_max\n", "486 Mary F 74 1940 2013 117.517027 4.915247\n", "61208 Gary M 74 1940 2013 47.176817 2.026460\n", "4069 Kylie F 50 1960 2013 5.152602 0.304518\n", "877 Katina F 74 1940 2013 0.713220 0.180452\n", "1146 Kara F 74 1940 2013 5.586111 0.174883\n", "1128 Kari F 74 1940 2013 3.452775 0.152163\n", "2037 Kyla F 67 1940 2013 1.973170 0.111073\n", "61970 Garry M 74 1940 2013 2.445214 0.108674\n", "995 Kyra F 74 1940 2013 1.803754 0.103624\n", "61798 Daryl M 74 1940 2013 3.173102 0.096532\n", "--------------------\n", "Dataframe yob filtered to those that match list (count only)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1425795 records to begin.\n", "5732 records remaining." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "--------------------\n", "Head of total matching list per year, female\n", " births pct\n", "year \n", "1940 56928 4.979183\n", "1941 58775 4.865895\n", "1942 64152 4.750103\n", "1943 67105 4.810263\n", "1944 63425 4.779308\n", "\n", "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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9WullrLbj79j274GNMBv/SKWNsBrJ9733zT3P+JQgpkOAXZ0NGvMD9rvaDju3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\ngWVZl1mWVZ7CfCJSh4WuvIZI70Pwv/EaOXeMS3c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Wa7d0RxIRERHJGMl+lF4GDDNN8w/g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ksvNTB+fJIXXggxeXKU008v46GHQjqHVURERLJGQVVERFoZMcLlqacamTWrgc9/PsmKFSbXXlvC\ncceVc8UVJbz+egDXzXUrRUREpDNTUBURkX0YBnzhCw5/+lMjNTX1zJwZ43Of8/jb30Kcd14ZJ54Y\n5ec/D/Phh3k7y5mIiIgUMAVVERH5TL17e1x5ZZyFC+t5+ukGvvrVBNu2Gfz0pxGOPz7Kl79cypNP\nBmlqynVLRUREpLNQUBURkTYxDBg/3uGee5pYvbqOu+9u5IQTHP7+9yCXXVbK8OHlXH011Naql1VE\nREQOj4KqiIgctPJyuOCCJM8+28iCBXV873sxIhGPu+6CceOi3HtviFgs160UERGRQqWgKiIih2Xg\nQI///u84y5fXc/fdYJpw660lnHxylKefDuJ5uW6hiIiIFBoFVRERaRfhMFx1FSxeXMdll8X55z8N\nLrmklLPPLmPpUr3diIiISNvpk4OIiLSrykq47bYYr79ez1lnJXjrrQBnnhnl0ktL2LxZ56+KiIjI\ngSmoiohIh+jf3+OPf2zi6acbGD3aYc6cECefHOUHPwize3euWyciIiL5TEFVREQ61PjxDnPnNnDf\nfY1UV3v86lcRxo2L8sADIZLJXLdORERE8pGCqoiIdDjThPPPT7JgQT033RQjFjO44YYSTj21jMcf\nDyqwioiISCsKqiIikjWlpXDVVXEWLarnm9+M8/77JpdfXsrJJ0d55JEg8XiuWygiIiL5QEFVRESy\nrmdPj5/9LMaiRfVceGGcLVsMvv/9UsaPj/KHP4Roasp1C0VERCSXFFRFRCRn+vXz+PnPY7z1Vj2X\nXRanttbgxhtLGDs2yn33hairy3ULRUREJBcUVEVEJOd69/a47bYYS5fWc+WVMerrDW65xQ+sd92l\nUYJFRESKjYKqiIjkjR49PGbOjLN8eR3XXhvDdQ1+9KMIY8aU8+Mfh6mt1TysIiIixUBBVURE8k5l\nJVx7bZxly+qYOTNGOOxx550Rjj8+yg03RLBtvX2JiIh0ZnqnFxGRvNWlC1x5ZZylS+u5/fYmunXz\neOCBMBMnRjn//FJeeCGA4+S6lSIiItLeFFRFRCTvlZXBpZcmWLq0nj/8oZGTTkry2mtBLrywjHHj\notx7b4gdO3LdShEREWkvCqoiIlIwgkGYMSPJnDmNzJ9fzze+EWfrVoNbby1h1Khyrr46wpo1emsT\nEREpdHo3FxGRgnTssS6/+EWMlSvruOWWJnr08Jg9O8yUKVHOOaeUZ54JkkjkupUiIiJyKBRURUSk\noHXrBldckWDx4npmz25g8uQkCxcGufjiUsaOjfLDH4ZZscLE83LdUhEREWkrBVUREekUAgGYPt3h\nr39tZMGCOi65JM6ePQb33BNh+vQoxx8fZebMCAsXagAmERGRfKegKiIinc7AgR533BFj9eo6Hnyw\nkS9/OcHu3Qa/+12Yc84pY/jwKFdfHeGVVwLEYrlurYiIiOwtmOsGiIiIdJSyMjjzzCRnnpkkHoc3\n3wzw3HNB5s4NMnt2mNmzw3Tp4vH5zyc566wkU6cmiUZz3WoRERFRUBURkaIQDsOUKQ5Tpjj85Ccx\nli71Q+vzzwd54okQTzwRoqTEY8IEh9GjHUaNchg92qVXL53cKiIikm0KqiIiUnQCARg3zmHcOIdb\nb42xerXJc88Fee65IPPn+0vaEUe4zaF11CiHkSMdunfPYeNFRESKgIKqiIgUNcOA4cNdhg+Pc8MN\ncWprDVauNKmpCTSvX3ghxAsvZO7Tr5/L6NF+aB071mXsWIdAIHfPQUREpLNRUBUREWmhqspj6lSH\nqVMzQwN/9JHRKriuWBFgzpwQc+aEAKiudjnjDP8814kTHcLhXLVeRESkc1BQFREROYAjjvA444wk\nZ5zhX/Y82LzZYMWKAK+9FmDu3CCzZoWZNStMRYXH9OlJzj47yeTJSUpLc9t2ERGRQqSgKiIicpAM\nA/r18+jXL8k55yT56U9jLFkSaD7P9bHHQjz2WIiyMo9p0/zQOm1akvLyXLdcRESkMCioioiIHKZA\nACZMcJgwweG222LU1PiDMz37bIinn/aXSMRj8mSHM89MMG6cwzHHeBhGrlsuIiKSnxRURURE2pFh\nwJgxLmPGxJk5M866dSbPPuv3tL74or8AVFR4jByZngrHH1H4c59TeBUREQEFVRERkQ5jGDBsmMuw\nYXGuuy7Oe+8ZvPJKkOXLA6nzW4O89lrmrbhHD7d5Gpx0gK2q0jyuIiJSfBRURUREsmTAAI8BAxJA\nAoCdO2HlSj+01tSYrFwZYN68IPPmZd6e+/Z1mTgxybRpDpMn6zxXEREpDgqqIiIiOdKtG5x6qsOp\np2amwvn4Y6PVNDg1NSaPPBLmkUcgFPKYMMFh+nR/cKb+/dXbKiIinZOCqoiISB7p1ctj+nSH6dP9\n8Oq6sGKFybx5QV5+Odh8uPDMmTBwoMO0aX5wHTfOIRTKceNFRETaiYKqiIhIHjPNzOBMN9wQ56OP\nDF5+Oci8ef45rr/5TYDf/CZMly4eU6b4Pa2nnKKBmUREpLApqIqIiBSQI47w+PrXE3z96wmammDB\ngkAquAabp8IBqK52GT7cZcQIhxEjXIYPd+jXT+FVREQKg4KqiIhIgSopgalTHaZOdfjhD2Ns2GAy\nb16AZcsCrFoVYP78IPPnZ97qKyo8hg93mgPsyJEO/ft7mGYOn4SIiMh+KKiKiIh0AoYBluViWS7p\nUYV37IBVqwK8/baZWgd4440gb7yRuV806nHssQ7HHeemFochQ1xKSnLzPEREREBBVUREpNOqrIRJ\nkxwmTXJIh9c9e2DNGj+8vv12gFWrTJYuDbBkSeYjQSDgMWiQy7HH+sE1HWI1p6uIiGSLgqqIiEgR\n6dIFxo93GD8+E14bG+Gdd0xWrw6werXJ6tUma9YEWL8+wOOPZ4YS7t3bD6xjxkBZWYjKSo9u3Ty6\ndaPFtkckkqMnJyIinYaCqoiISJErLYVRo1xGjXKbr3Nd2LTJYM2adHj11y+9FOSllwA+/djgsjKv\nObhWVvrL4MEuY8Y4jBrlUl2tnlkREflsCqoiIiKyD9OE/v09+vdPMmNG5vpt2wy2bStn48ZGdu6E\nHTsMdu409lnv3Gnwj3+YrFnjDzP8zDOZ39G3rx9aR492GD3aH5E4Gs3yExQRkbymoCoiIiJtVl3t\nMXQoDBmSbNPtk0morTVYtcpk+fIANTUBampM5swJMWeOf1ixaXoMGZIOry6jRzsMHuwSCh3gl4uI\nSKeloCoiIiIdJhiEXr08evVymDbNAcDzYPNmg5qaQCq8+gM7rV0bYPZs/37hsIdl+QM6DRvmpNY6\nbFhEpFgoqIqIiEhWGQb06+fRr1+SL33J75lNJmH9erO5x3X16gDr1/tryHSt9uzpNofWY491GDbM\nZdAg9b6KiHQ2CqoiIiKSc8EgzdPgfOMb/nWOA++/b7J2rb+sWRNg7VqT+fODzJ+fuW8o5E+nM2yY\ny9Chfg/s0KEuvXt7GEZuno+IiBweBVURERHJS4EADBrk95iec07m+p07Yd26QCq8+gH2nXdM1q4N\ntLp/t24eQ4Y4qfDqMnSov92lS5afiIiIHDQFVRERESko3brBhAkOEyY4zdc5DnzwgcHatQHWrTNT\nS4AlSwIsWtT6485RR/nht3t3fwqdigp/Cp2Kisy8sOk5YTUvrIhIbiioioiISMELBDLT6Zx9dub6\nxkawbT+4pkPs2rUmr77a9o9ApaV+iI1G/e2yMo+ysvQ2zZfLyjxKSzOXe/RwOfJIj6OOcqmo6IAn\nLSLSiSmoioiISKdVWgojR7qMHOkCmSl16uponvd11y5/3tdduwx27KD5cnpJX96zBz7+2KShAVz3\n4E5+7dLFo08fP7j26ePSp0/m8lFHufTq5REIHPj3iIgUCwVVERERKTrl5VBe7tGnz8FPd+N5EI9D\nQwM0NBg0Nvprf/G36+v9ULtli8GHH5p8+KG/Xrdu/wE3GPQYMMDlxBMdxo3zl759NRiUiBQvBVUR\nERGRg2AYEIn4S2VlOui2LfDu3k2r4PrhhwZbtpj84x/+IcnvvBNg1iz/tr17u82hddw4fyAo9bqK\nSLFQUBURERHJkq5dYdgwl2HDAJxWP0smYfVqk8WLAyxeHGDRogBz5oSYMyeUuq/HCSf4oXX8eIeR\nIx1KS7P/HEREskFBVURERCQPBIMwapTLqFEul12WwPNg40aDRYsCLF4cZNGiAK+8EuSVV/yPb6bp\nceSRHkcf7XL00S7HHONy9NFeau0Sjeb4CYmIHAYFVREREZE8ZBiZkYwvuMAfCOrjjw2WLPF7XFeu\nNNm0yeT114O8/vq+9+/ZMx1gveYgO2iQy8CBrnpiRSTvKaiKiIiIFIhevTxmzEgyY0ZmBOOGBvjg\nA5ONG002bTJSa//y0qUBlixpPSKTYXj07esxeLCLZTlYltu8lJdn+xmJiOyfgqqIiIhIASsrg6FD\nXYYOdff5WSIB//iHwaZNJu+/b2LbmWXevCDz5rX+KHjkkW6r4Dp2LHTvbtCzp0YgFpHsUlAVERER\n6aRCofThww5Tp7YevKm21mDDBpN33mkdYOfPDzJ/fstblhONevTv7zJggEv//pllwACXysqsPiUR\nKRIKqiIiIiJFqKrKo6rKH0G4pd27aQ6tH31UyqpVCd5/32TDBpNVq/adH6ey0msVXNPrY47RocQi\ncugUVEVERESkWdeuMHasy9ixLj16wNatTQC4LvzznwbvvecfRvzee/55sO+/b7BypcmyZfuG2F69\n9u6F9RgwwB/kKRLJ9jMTkUKioCoiIiIiB2SacOSRHkce6TBp0r5zwG7ebKSCqx9i00F24cIACxYE\n9/pdHn36+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"text": [ "" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "Tail of dataframe:\n", " name sex births year pct ranked temp\n", "1782436 Cary M 22 2013 0.001176 4349.5 1\n", "1782836 Oran M 20 2013 0.001069 4667.0 1\n", "1783028 Raja M 19 2013 0.001015 4852.5 1\n", "1784217 Marin M 14 2013 0.000748 6041.0 1\n", "1787021 Darel M 8 2013 0.000427 9152.0 1\n", "Tail of dataframe:" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " name sex births year pct ranked temp\n", "1770377 Katina F 9 2013 0.000518 11386 1\n", "1773034 Marry F 7 2013 0.000403 13757 1\n", "1773893 Cary F 6 2013 0.000345 15448 1\n", "1776102 Daryl F 5 2013 0.000288 17768 1\n", "1777187 Marlana F 5 2013 0.000288 17768 1\n", "Warning: colors will be repeated.\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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0KPr06YMnT55AEAR06dJFZRqGhobi5wKgNGWToaGhuE1pFbyuSqpfv37Ys2cP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\niiI9PZ327dvTo0cPsrOzK61rMpno2bMnS5YsYcKECbRu3Zp77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2Lb788kvxM21tbaU3FfLOpyRsbGwAvN+XGzRogJkzZ2LXrl0KbcNJ9ZigUrEU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HFd8YKgnKRpoLuywsknH+fE43nAeuKJx+guL59z2zSO8/WPHyrr8nNo97HjxpuojY0ThCFBtYpf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\n5cgPSRJCiNvkiy++IC4ujhdeeMHZoQghhBBCuCTZQ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"text": [ "" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "print names_listed.name.unique()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "['Mary' 'Gary' 'Kylie' 'Katina' 'Kara' 'Kari' 'Kyla' 'Garry' 'Kyra' 'Daryl'\n", " 'Cara' 'Alina' 'Catina' 'Marla' 'Mayra' 'Kira' 'Marina' 'Ira' 'Marlee'\n", " 'Cary' 'Darrel' 'Nyah' 'Caryn' 'Callen' 'Mara' 'Carie' 'Marin' 'Maira'\n", " 'Callan' 'Lyra' 'Karri' 'Yara' 'Tana' 'Kylene' 'Carri' 'Marlana' 'Kirra'\n", " 'Alba' 'Kye' 'Calla' 'Tarra' 'Marah' 'Arika' 'Arica' 'Calen' 'Toma'\n", " 'Kawana' 'Marly' 'Nada' 'Oran' 'Meta' 'Calin' 'Darel' 'Kania' 'Calan'\n", " 'Kanya' 'Marinna' 'Raja' 'Mee' 'Tamora' 'Junia' 'Moria' 'Marry' 'Jara'\n", " 'Garrie' 'Kalinda' 'Merinda' 'Macala' 'Kalla' 'Marlea' 'Minta' 'Marine'\n", " 'Jarah' 'Merina' 'Nain' 'Jeda' 'Arora' 'Marra' 'Parry' 'Marea' 'Darral'\n", " 'Karrin' 'Naturi' 'Karia' 'Yani' 'Carrin' 'Meryn' 'Callin' 'Metta' 'Burel'\n", " 'Caran' 'Kalyan' 'Daril' 'Burrel' 'Koron' 'Merrin' 'Yania' 'Nanda' 'Maran'\n", " 'Adoni' 'Lira' 'Keina' 'Maryna' 'Kimba' 'Maron' 'Elanora' 'Darril' 'Kolya'\n", " 'Mori' 'Narelle' 'Macalla' 'Hanya' 'Jarrah' 'Marrin' 'Patia' 'Bardo'\n", " 'Nerida' 'Moona' 'Juni' 'Ingar' 'Bara' 'Akala' 'Taree' 'Urana' 'Dural'\n", " 'Daral' 'Garri' 'Jannali' 'Gyra' 'Bunyan' 'Callon' 'Amarina' 'Niree'\n", " 'Amarin' 'Parri' 'Gurley' 'Akam' 'Bonell']\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Refine list #\n", "\n", "Some of these names are only coincidentally mythological, e.g. Seth is an Egyptian god's name, but a common Hebrew name, and Doris is a very minor mythological figure, so probably few parents were even aware of the connection (same with Phoebe, Chlore ... Diana is a tougher call, but I think parents are more likely to not have used the name because of a mythological association). Obviously, it's impossible to reading parents' minds with data abstracted like this, so the only choice is to manually curate names that most obviously come from mythology." ] }, { "cell_type": "code", "collapsed": false, "input": [ "cutoffn = 0\n", "# how many names will remain to evaluate after duplicates removed\n", "\n", "from collections import OrderedDict\n", "evallistm = OrderedDict()\n", "evallistf = OrderedDict()\n", "\n", "# remove names with more common duplicates in other sex\n", "# this happens frequently in ssa db\n", "\n", "for name in listed_m:\n", " try:\n", " pctf = names_listed[(names_listed.sex == 'F') & \n", " (names_listed.name == name)].pct_max.iloc[0]\n", " pctm = names_listed[(names_listed.sex == 'M') & \n", " (names_listed.name == name)].pct_max.iloc[0]\n", " except:\n", " pctf = 98\n", " pctm = 99\n", " if (name not in names_listed[names_listed.sex == 'F'].name.unique() or\n", " pctf < pctm):\n", " evallistm[name] = ''\n", " \n", "for name in listed_f:\n", " try:\n", " pctf = names_listed[(names_listed.sex == 'F') & \n", " (names_listed.name == name)].pct_max.iloc[0]\n", " pctm = names_listed[(names_listed.sex == 'M') & \n", " (names_listed.name == name)].pct_max.iloc[0]\n", " except:\n", " pctf = 99\n", " pctm = 98\n", " if (name not in names_listed[names_listed.sex == 'M'].name.unique() or\n", " pctm < pctf):\n", " evallistf[name] = ''\n", " \n", "if cutoffn > 0:\n", " assert len(evallistm) > cutoffn\n", " assert len(evallistf) > cutoffn\n", " print evallistm[:cutoffn]\n", " print evallistf[:cutoffn]\n", "else:\n", " print 'Length of lists: %d male, %d female\\n' % (len(evallistm), len(evallistf))\n", " print evallistm\n", " print ' '\n", " print evallistf" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Length of lists: 40 male, 98 female\n", "\n", "OrderedDict([('Ira', ''), ('Gary', ''), ('Darrel', ''), ('Darel', ''), ('Oran', ''), ('Daryl', ''), ('Garry', ''), ('Cary', ''), ('Kye', ''), ('Parry', ''), ('Callen', ''), ('Raja', ''), ('Darral', ''), ('Callan', ''), ('Calen', ''), ('Calin', ''), ('Garrie', ''), ('Toma', ''), ('Calan', ''), ('Maron', ''), ('Nain', ''), ('Koron', ''), ('Daril', ''), ('Callin', ''), ('Kalyan', ''), ('Darril', ''), ('Adoni', ''), ('Burrel', ''), ('Bardo', ''), ('Burel', ''), ('Daral', ''), ('Callon', ''), ('Kolya', ''), ('Bunyan', ''), ('Bara', ''), ('Amarin', ''), ('Dural', ''), ('Akam', ''), ('Gurley', ''), ('Bonell', '')])\n", " \n", "OrderedDict([('Caryn', ''), ('Alba', ''), ('Tana', ''), ('Marlee', ''), ('Mary', ''), ('Mara', ''), ('Marla', ''), ('Katina', ''), ('Nada', ''), ('Kyra', ''), ('Marlana', ''), ('Kari', ''), ('Kara', ''), ('Marina', ''), ('Cara', ''), ('Marry', ''), ('Meta', ''), ('Marlea', ''), ('Alina', ''), ('Kyla', ''), ('Calla', ''), ('Mayra', ''), ('Maira', ''), ('Kira', ''), ('Karri', ''), ('Marin', ''), ('Tarra', ''), ('Marea', ''), ('Carie', ''), ('Kylene', ''), ('Marine', ''), ('Moria', ''), ('Lyra', ''), ('Marly', ''), ('Kylie', ''), ('Carri', ''), ('Merinda', ''), ('Arica', ''), ('Minta', ''), ('Catina', ''), ('Marah', ''), ('Jara', ''), ('Arika', ''), ('Yara', ''), ('Karia', ''), ('Karrin', ''), ('Tamora', ''), ('Junia', ''), ('Kanya', ''), ('Jarah', ''), ('Kirra', ''), ('Kawana', ''), ('Kalinda', ''), ('Metta', ''), ('Carrin', ''), ('Marra', ''), ('Marinna', ''), ('Nyah', ''), ('Kania', ''), ('Merina', ''), ('Kalla', ''), ('Mee', ''), ('Maryna', ''), ('Keina', ''), ('Merrin', ''), ('Yani', ''), ('Caran', ''), ('Jeda', ''), ('Meryn', ''), ('Kimba', ''), ('Macala', ''), ('Lira', ''), ('Arora', ''), ('Yania', ''), ('Nanda', ''), ('Jarrah', ''), ('Mori', ''), ('Patia', ''), ('Hanya', ''), ('Naturi', ''), ('Nerida', ''), ('Juni', ''), ('Marrin', ''), ('Akala', ''), ('Taree', ''), ('Maran', ''), ('Narelle', ''), ('Jannali', ''), ('Elanora', ''), ('Urana', ''), ('Parri', ''), ('Amarina', ''), ('Gyra', ''), ('Niree', ''), ('Macalla', ''), ('Garri', ''), ('Ingar', ''), ('Moona', '')])\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "#manually copy and paste the above lists and assign \n", "#'acc' or 'rej' individually to accept or reject\n", "\n", "evallistm = OrderedDict([('Sol', 'rej'), ('Seth', 'rej'), ('Griffin', 'rej'), ('Amon', 'rej'), \n", " ('Thor', 'acc'), ('Hercules', 'acc'), ('Ladon', 'rej'), ('Odin', 'acc'), \n", " ('Hermes', 'acc'), ('Apollo', 'acc'), ('Osiris', 'acc'), ('Min', 'rej'), \n", " ('Clete', 'rej'), ('Zeus', 'acc'), ('Phoenix', 'acc'), ('Amen', 'rej'), \n", " ('Mars', 'acc'), ('Ares', 'acc'), ('Loki', 'acc'), ('Nike', 'rej'), \n", " ('Ran', 'rej'), ('Mercury', 'acc'), ('Tyr', 'acc'), ('Jupiter', 'acc'), \n", " ('Kore', 'rej'), ('Ra', 'acc'), ('Anubis', 'acc'), ('Helios', 'acc'), \n", " ('Poseidon', 'acc'), ('Makar', 'rej'), ('Pater', 'rej'), ('Amun', 'rej'), \n", " ('Fenris', 'acc'), ('Set', 'rej'), ('Demeter', 'rej'), ('Horus', 'acc'), \n", " ('Megale', 'rej'), ('Aten', 'acc'), ('Saturn', 'acc')])\n", " \n", "evallistf = OrderedDict([('Athena', 'acc'), ('Delia', 'rej'), ('Minerva', 'acc'), ('Doris', 'rej'), ('Phoebe', 'rej'), \n", " ('Chloe', 'rej'), ('Diana', 'rej'), ('Flora', 'rej'), ('Sophia', 'rej'), \n", " ('Rhea', 'rej'), ('Venus', 'acc'), ('Vesta', 'acc'), ('Luna', 'rej'),\n", " ('Thalia', 'acc'), ('Lucina', 'rej'), ('Gerda', 'rej'), ('Eris', 'acc'), \n", " ('Artemis', 'acc'), ('Aphrodite', 'acc'), ('Isis', 'acc'), ('Clio', 'acc'), \n", " ('Persephone', 'acc'), ('Melaina', 'rej'), ('Shai', 'rej'), ('Andromeda', 'acc'), \n", " ('Lamia', 'acc'), ('Sia', 'rej'), ('Hera', 'acc'), ('Nanna', 'rej'), ('Urania', 'acc'), \n", " ('Gaia', 'acc'), ('Khloe', 'rej'), ('Chloris', 'rej'), ('Athene', 'acc'), \n", " ('Janus', 'rej'), ('Freyja', 'acc'), ('Valkyrie', 'acc'), ('Ourania', 'acc'), \n", " ('Juno', 'acc'), ('Vali', 'acc'), ('Holle', 'rej'), ('Cybele', 'acc'), \n", " ('Pelagia', 'rej'), ('Anat', 'rej'), ('Soteria', 'rej'), ('Pallas', 'acc'), \n", " ('Fortuna', 'rej'), ('Maat', 'acc'), ('Caliope', 'acc'), ('Chimera', 'acc'), \n", " ('Deianeira', 'rej'), ('Agathe', 'rej'), ('Lousia', 'rej'), ('Shu', 'rej'), \n", " ('Areion', 'rej'), ('Ceres', 'acc'), ('Areia', 'rej'), ('Saturn', 'rej'), \n", " ('Lakinia', 'rej'), ('Tyche', 'acc'), ('Khepri', 'acc'), ('Demeter', 'acc'),\n", " ('Nike', 'acc')])\n", "\n", "# Note, Demeter and Nike taken from males' list and moved to females'; for some reason it spiked in males higher than in females\n", "# Similarly, Saturn moved from females' to males'\n", "\n", "# Test that all names have 'acc' or 'rej' values\n", "\n", "final_m = []\n", "final_f = []\n", "\n", "names_not_validated = []\n", "for item in evallistm:\n", " if evallistm[item] not in ['acc', 'rej']:\n", " names_not_validated.append(item)\n", " elif evallistm[item] == 'acc':\n", " final_m.append(item)\n", "for item in evallistf:\n", " if evallistf[item] not in ['acc', 'rej']:\n", " names_not_validated.append(item)\n", " elif evallistf[item] == 'acc':\n", " final_f.append(item)\n", " \n", "final_all = final_m + final_f\n", "\n", "if len(names_not_validated) > 0:\n", " print \"The following names do not have 'acc' or 'rej' values: \", names_not_validated\n", " raise exception(\"Names not validated\")\n", " \n", "print 'Accepted male names:', final_m\n", "print 'Accepted female names:', final_f\n", "\n", "print 'Length: %d male, %d female\\n' % (len(final_m), len(final_f))\n", "\n", "cutmin = min(len(final_m), len(final_f))\n", "\n", "final_m = final_m[:cutmin]\n", "final_f = final_f[:cutmin]\n", "\n", "print 'After resizing to %d names each:' % (cutmin)\n", "print 'Accepted male names:', final_m\n", "print 'Accepted female names:', final_f" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Accepted male names: ['Thor', 'Hercules', 'Odin', 'Hermes', 'Apollo', 'Osiris', 'Zeus', 'Phoenix', 'Mars', 'Ares', 'Loki', 'Mercury', 'Tyr', 'Jupiter', 'Ra', 'Anubis', 'Helios', 'Poseidon', 'Fenris', 'Horus', 'Aten', 'Saturn']\n", "Accepted female names: ['Athena', 'Minerva', 'Venus', 'Vesta', 'Thalia', 'Eris', 'Artemis', 'Aphrodite', 'Isis', 'Clio', 'Persephone', 'Andromeda', 'Lamia', 'Hera', 'Urania', 'Gaia', 'Athene', 'Freyja', 'Valkyrie', 'Ourania', 'Juno', 'Vali', 'Cybele', 'Pallas', 'Maat', 'Caliope', 'Chimera', 'Ceres', 'Tyche', 'Khepri', 'Demeter', 'Nike']\n", "Length: 22 male, 32 female\n", "\n", "After resizing to 22 names each:\n", "Accepted male names: ['Thor', 'Hercules', 'Odin', 'Hermes', 'Apollo', 'Osiris', 'Zeus', 'Phoenix', 'Mars', 'Ares', 'Loki', 'Mercury', 'Tyr', 'Jupiter', 'Ra', 'Anubis', 'Helios', 'Poseidon', 'Fenris', 'Horus', 'Aten', 'Saturn']\n", "Accepted female names: ['Athena', 'Minerva', 'Venus', 'Vesta', 'Thalia', 'Eris', 'Artemis', 'Aphrodite', 'Isis', 'Clio', 'Persephone', 'Andromeda', 'Lamia', 'Hera', 'Urania', 'Gaia', 'Athene', 'Freyja', 'Valkyrie', 'Ourania', 'Juno', 'Vali']\n" ] } ], "prompt_number": 9 }, { "cell_type": "heading", "level": 3, "metadata": {}, "source": [ "Redo last block because Saturn, Nike and Demeter were assigned to wrong sex\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from copy import deepcopy\n", "oldm = deepcopy(evallistm)\n", "oldf = deepcopy(evallistf)\n", "\n", "cutoffn = 0\n", "# how many names will remain to evaluate after duplicates removed\n", "\n", "from collections import OrderedDict\n", "evallistm = OrderedDict()\n", "evallistf = OrderedDict()\n", "\n", "# remove names with more common duplicates in other sex\n", "# this happens frequently in ssa db\n", "\n", "for name in listed_m:\n", " try:\n", " pctf = names_listed[(names_listed.sex == 'F') & \n", " (names_listed.name == name)].pct_max.iloc[0]\n", " pctm = names_listed[(names_listed.sex == 'M') & \n", " (names_listed.name == name)].pct_max.iloc[0]\n", " except:\n", " pctf = 98\n", " pctm = 99\n", " if (name not in ['Demeter', 'Nike'] and (name not in names_listed[names_listed.sex == 'F'].name.unique() or\n", " pctf < pctm or name == 'Saturn')):\n", " evallistm[name] = ''\n", " \n", "for name in listed_f:\n", " try:\n", " pctf = names_listed[(names_listed.sex == 'F') & \n", " (names_listed.name == name)].pct_max.iloc[0]\n", " pctm = names_listed[(names_listed.sex == 'M') & \n", " (names_listed.name == name)].pct_max.iloc[0]\n", " except:\n", " pctf = 99\n", " pctm = 98\n", " if (name != 'Saturn' and (name not in names_listed[names_listed.sex == 'M'].name.unique() or\n", " pctm < pctf or name in ['Demeter', 'Nike'])):\n", " evallistf[name] = ''\n", "\n", "for item in evallistm: # copy from above block\n", " try:\n", " evallistm[item] = oldm[item]\n", " except:\n", " pass\n", "for item in evallistf:\n", " try:\n", " evallistf[item] = oldf[item]\n", " except:\n", " pass \n", " \n", " \n", "if cutoffn > 0:\n", " assert len(evallistm) > cutoffn\n", " assert len(evallistf) > cutoffn\n", " print evallistm[:cutoffn]\n", " print evallistf[:cutoffn]\n", "else:\n", " print 'Length of lists: %d male, %d female\\n' % (len(evallistm), len(evallistf))\n", " print evallistm\n", " print ' '\n", " print evallistf" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Length of lists: 36 male, 61 female\n", "\n", "OrderedDict([('Sol', 'rej'), ('Seth', 'rej'), ('Griffin', 'rej'), ('Amon', 'rej'), ('Thor', 'acc'), ('Hercules', 'acc'), ('Ladon', 'rej'), ('Odin', 'acc'), ('Hermes', 'acc'), ('Apollo', 'acc'), ('Osiris', 'acc'), ('Min', 'rej'), ('Clete', 'rej'), ('Zeus', 'acc'), ('Phoenix', 'acc'), ('Amen', 'rej'), ('Mars', 'acc'), ('Ares', 'acc'), ('Loki', 'acc'), ('Ran', 'rej'), ('Mercury', 'acc'), ('Tyr', 'acc'), ('Jupiter', 'acc'), ('Kore', 'rej'), ('Ra', 'acc'), ('Anubis', 'acc'), ('Helios', 'acc'), ('Poseidon', 'acc'), ('Makar', 'rej'), ('Pater', 'rej'), ('Amun', 'rej'), ('Fenris', 'acc'), ('Set', 'rej'), ('Horus', 'acc'), ('Megale', 'rej'), ('Aten', 'acc')])\n", " \n", "OrderedDict([('Delia', 'rej'), ('Minerva', 'acc'), ('Doris', 'rej'), ('Phoebe', 'rej'), ('Chloe', 'rej'), ('Diana', 'rej'), ('Flora', 'rej'), ('Sophia', 'rej'), ('Rhea', 'rej'), ('Venus', 'acc'), ('Vesta', 'acc'), ('Luna', 'rej'), ('Thalia', 'acc'), ('Lucina', 'rej'), ('Athena', 'acc'), ('Gerda', 'rej'), ('Eris', 'acc'), ('Artemis', 'acc'), ('Aphrodite', 'acc'), ('Isis', 'acc'), ('Clio', 'acc'), ('Persephone', 'acc'), ('Melaina', 'rej'), ('Shai', 'rej'), ('Andromeda', 'acc'), ('Lamia', 'acc'), ('Sia', 'rej'), ('Hera', 'acc'), ('Nanna', 'rej'), ('Urania', 'acc'), ('Gaia', 'acc'), ('Khloe', 'rej'), ('Chloris', 'rej'), ('Athene', 'acc'), ('Nike', 'acc'), ('Janus', 'rej'), ('Freyja', 'acc'), ('Valkyrie', 'acc'), ('Ourania', 'acc'), ('Juno', 'acc'), ('Vali', 'acc'), ('Holle', 'rej'), ('Cybele', 'acc'), ('Pelagia', 'rej'), ('Anat', 'rej'), ('Soteria', 'rej'), ('Pallas', 'acc'), ('Fortuna', 'rej'), ('Maat', 'acc'), ('Caliope', 'acc'), ('Chimera', 'acc'), ('Deianeira', 'rej'), ('Agathe', 'rej'), ('Lousia', 'rej'), ('Shu', 'rej'), ('Areion', 'rej'), ('Ceres', 'acc'), ('Areia', 'rej'), ('Lakinia', 'rej'), ('Tyche', 'acc'), ('Khepri', 'acc')])\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "#manually copy and paste the above lists and assign \n", "#'acc' or 'rej' individually to accept or reject\n", "\n", "# 72, 29 and 80 character rule (PEP) do not reach this ->|\n", "#########1#########2#########3#########4#########5#########6#########7#2######9X\n", "\n", "evallistm = OrderedDict([('Sol', 'rej'), ('Seth', 'rej'), ('Griffin', 'rej'), \n", " ('Amon', 'rej'), ('Thor', 'acc'), ('Hercules', 'acc'), \n", " ('Ladon', 'rej'), ('Odin', 'acc'), ('Hermes', 'acc'), \n", " ('Apollo', 'acc'), ('Osiris', 'acc'), ('Min', 'rej'), \n", " ('Clete', 'rej'), ('Zeus', 'acc'), ('Phoenix', 'acc'),\n", " ('Amen', 'rej'), ('Mars', 'acc'), ('Ares', 'acc'), \n", " ('Loki', 'acc'), ('Ran', 'rej'), ('Mercury', 'acc'),\n", " ('Tyr', 'acc'), ('Jupiter', 'acc'), ('Kore', 'rej'),\n", " ('Ra', 'acc'), ('Anubis', 'acc'), ('Helios', 'acc'),\n", " ('Poseidon', 'acc'), ('Makar', 'rej'),\n", " ('Pater', 'rej'), ('Amun', 'rej'), ('Fenris', 'acc'),\n", " ('Set', 'rej'), ('Horus', 'acc'), ('Megale', 'rej'),\n", " ('Aten', 'acc')])\n", " \n", "evallistf = OrderedDict([('Athena', 'acc'), ('Delia', 'rej'), ('Minerva', 'acc'), ('Doris', 'rej'),\n", " ('Phoebe', 'rej'), ('Chloe', 'rej'), ('Diana', 'rej'),\n", " ('Flora', 'rej'), ('Sophia', 'rej'), ('Rhea', 'rej'),\n", " ('Venus', 'acc'), ('Vesta', 'acc'), ('Luna', 'rej'), \n", " ('Thalia', 'acc'), ('Lucina', 'rej'), ('Gerda', 'rej'), \n", " ('Eris', 'acc'), ('Artemis', 'acc'), \n", " ('Aphrodite', 'acc'), ('Isis', 'acc'), ('Clio', 'acc'),\n", " ('Persephone', 'acc'), ('Melaina', 'rej'), \n", " ('Shai', 'rej'), ('Andromeda', 'acc'), \n", " ('Lamia', 'acc'), ('Sia', 'rej'), ('Hera', 'acc'),\n", " ('Nanna', 'rej'), ('Urania', 'acc'), ('Gaia', 'acc'),\n", " ('Khloe', 'rej'), ('Chloris', 'rej'),\n", " ('Athene', 'acc'), ('Nike', 'acc'), ('Janus', 'rej'),\n", " ('Freyja', 'acc'), ('Valkyrie', 'acc'), \n", " ('Ourania', 'acc'), ('Juno', 'acc'), ('Vali', 'acc'),\n", " ('Holle', 'rej'), ('Cybele', 'acc'), ('Pelagia', 'rej'),\n", " ('Anat', 'rej'), ('Soteria', 'rej'), ('Pallas', 'acc'),\n", " ('Fortuna', 'rej'), ('Maat', 'acc'), ('Caliope', 'acc'),\n", " ('Chimera', 'acc'), ('Deianeira', 'rej'),\n", " ('Agathe', 'rej'), ('Lousia', 'rej'), ('Shu', 'rej'),\n", " ('Areion', 'rej'), ('Ceres', 'acc'), ('Areia', 'rej'),\n", " ('Lakinia', 'rej'), ('Tyche', 'acc'), ('Khepri', 'acc')])\n", "\n", "# Note, Demeter and Nike taken from males' list and moved to females'; for some reason it spiked in males higher than in females\n", "# Similarly, Saturn moved from females' to males'\n", "\n", "# Test that all names have 'acc' or 'rej' values\n", "\n", "final_m = []\n", "final_f = []\n", "\n", "names_not_validated = []\n", "for item in evallistm:\n", " if evallistm[item] not in ['acc', 'rej']:\n", " names_not_validated.append(item)\n", " elif evallistm[item] == 'acc':\n", " final_m.append(item)\n", "for item in evallistf:\n", " if evallistf[item] not in ['acc', 'rej']:\n", " names_not_validated.append(item)\n", " elif evallistf[item] == 'acc':\n", " final_f.append(item)\n", "\n", "if len(names_not_validated) > 0:\n", " print \"The following names do not have 'acc' or 'rej' values: \", names_not_validated\n", " raise exception(\"Names not validated\")\n", " \n", "print 'Accepted male names:', final_m\n", "print 'Accepted female names:', final_f\n", "\n", "print 'Length: %d male, %d female\\n' % (len(final_m), len(final_f))" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Accepted male names: ['Thor', 'Hercules', 'Odin', 'Hermes', 'Apollo', 'Osiris', 'Zeus', 'Phoenix', 'Mars', 'Ares', 'Loki', 'Mercury', 'Tyr', 'Jupiter', 'Ra', 'Anubis', 'Helios', 'Poseidon', 'Fenris', 'Horus', 'Aten']\n", "Accepted female names: ['Athena', 'Minerva', 'Venus', 'Vesta', 'Thalia', 'Eris', 'Artemis', 'Aphrodite', 'Isis', 'Clio', 'Persephone', 'Andromeda', 'Lamia', 'Hera', 'Urania', 'Gaia', 'Athene', 'Nike', 'Freyja', 'Valkyrie', 'Ourania', 'Juno', 'Vali', 'Cybele', 'Pallas', 'Maat', 'Caliope', 'Chimera', 'Ceres', 'Tyche', 'Khepri']\n", "Length: 21 male, 31 female\n", "\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# manually limit to nice round number\n", "\n", "nice_round_number = 100 # if too high, there will be no change\n", "final_m = final_m[:nice_round_number]\n", "final_f = final_f[:nice_round_number]\n", "\n", "print 'After manually resizing to nice round number of %d names each:' % (nice_round_number)\n", "print 'Accepted male names:', final_m\n", "print 'Accepted female names:', final_f" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "After manually resizing to nice round number of 100 names each:\n", "Accepted male names: ['Thor', 'Hercules', 'Odin', 'Hermes', 'Apollo', 'Osiris', 'Zeus', 'Phoenix', 'Mars', 'Ares', 'Loki', 'Mercury', 'Tyr', 'Jupiter', 'Ra', 'Anubis', 'Helios', 'Poseidon', 'Fenris', 'Horus', 'Aten']\n", "Accepted female names: ['Athena', 'Minerva', 'Venus', 'Vesta', 'Thalia', 'Eris', 'Artemis', 'Aphrodite', 'Isis', 'Clio', 'Persephone', 'Andromeda', 'Lamia', 'Hera', 'Urania', 'Gaia', 'Athene', 'Nike', 'Freyja', 'Valkyrie', 'Ourania', 'Juno', 'Vali', 'Cybele', 'Pallas', 'Maat', 'Caliope', 'Chimera', 'Ceres', 'Tyche', 'Khepri']\n" ] } ], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "# BTW, here's those missassigned (it appears) genders:\n", "print names[names.name == 'Saturn']\n", "print names[names.name == 'Demeter']\n", "print names[names.name == 'Nike']" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " name sex year_count year_min year_max pct_sum pct_max\n", "49524 Saturn F 2 1996 2001 0.000563 0.000285\n", " name sex year_count year_min year_max pct_sum pct_max\n", "95309 Demeter M 1 1920 1920 0.00047 0.00047\n", " name sex year_count year_min year_max pct_sum pct_max\n", "18008 Nike F 18 1953 2013 0.006075 0.000514\n", "76992 Nike M 15 1989 2013 0.005376 0.000951\n" ] } ], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "%run download_and_process.py\n", "\n", "# reduce names dataframe to those matching list\n", "# print '--------------------\\nDataframe names filtered to those that match list'\n", "# print \"%d records to begin.\" % (len(names))\n", "names_listed = names[((names.name.isin(final_m) & (names.sex == 'M')) |\n", " (names.name.isin(final_f) & (names.sex == 'F')) )].copy()\n", "names_listed.sort('pct_max', ascending=False, inplace=True)\n", "# print \"%d records remaining.\" % (len(names_listed))\n", "# listed_in_df = list(names_listed.name)\n", "# print names_listed.head(10)\n", "# listed_m = list(names[(names.sex == 'M') & (names.name.isin(final_m))]['name'])\n", "# listed_f = list(names[(names.sex == 'F') & (names.name.isin(final_f))]['name'])\n", "\n", "#reduce yob dataframe to those matching list\n", "print '--------------------\\nDataframe yob filtered to those that match list (count only)'\n", "print \"%d records to begin.\" % (len(yob))\n", "yob_listed_m = yob[(yob.name.isin(final_m)) & (yob.sex == 'M')].copy()\n", "yob_listed_m.sort(['year', 'sex', 'name'], ascending=False, inplace=True)\n", "yob_listed_f = yob[(yob.name.isin(final_f)) & (yob.sex == 'F')].copy()\n", "yob_listed_f.sort(['year', 'sex', 'name'], ascending=False, inplace=True)\n", "print \"%d records remaining.\" % (len(yob_listed))\n", "\n", "# m and f totals\n", "yob_listed_f_agg = pd.DataFrame(yob_listed_f.groupby('year').sum())[['births', 'pct']]\n", "yob_listed_m_agg = pd.DataFrame(yob_listed_m.groupby('year').sum())[['births', 'pct']]\n", "print '--------------------\\nHead of total matching list per year, female'\n", "print yob_listed_f_agg.head()\n", "\n", "# print chart of m and f totals\n", "print '\\n'\n", "\n", "# function to determine a nice y-axis limit a little above the maximum value\n", "# rounds maximum y up to second-most-significant digit\n", "def determine_y_limit(x): \n", " significance = int(math.floor((math.log10(x))))\n", " val = math.floor(x / (10 ** (significance - 1))) + 1\n", " val = val * (10 ** (significance - 1))\n", " return val\n", "\n", "#data\n", "xf = list(yob_listed_f_agg.index)\n", "xm = list(yob_listed_m_agg.index)\n", "\n", "plt.figure(figsize=(16,9))\n", "plt.plot(xf, list(yob_listed_f_agg.pct), color=\"red\")\n", "plt.plot(xm, list(yob_listed_m_agg.pct), color=\"blue\")\n", "\n", "plt.ylim(0, determine_y_limit(max(list(yob_listed_f_agg.pct)\n", " +list(yob_listed_m_agg.pct))))\n", "plt.xlim(1940, 2013)\n", "\n", "plt.title('Top 10 mythological names, boy=blue, girl=red', fontsize = 20)\n", "plt.xlabel(\"Year\", fontsize = 14)\n", "plt.ylabel(\"% of total births of that sex\", fontsize = 14)\n", "\n", "plt.show()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Data already downloaded.\n", "Data already extracted.\n", "Reading from pickle.\n", "Tail of dataframe 'yob':" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", " name sex births year pct ranked\n", "1792086 Zyhier M 5 2013 0.000267 12995\n", "1792087 Zylar M 5 2013 0.000267 12995\n", "1792088 Zymari M 5 2013 0.000267 12995\n", "1792089 Zymeer M 5 2013 0.000267 12995\n", "1792090 Zyree M 5 2013 0.000267 12995\n", "\n", "Tail of dataframe 'names':\n", " name sex year_count year_min year_max pct_sum pct_max\n", "102685 Gross M 1 1925 1925 0.000538 0.000538\n", "102686 Elik M 1 2012 2012 0.000318 0.000318\n", "102687 Patrickjoseph M 1 1998 1998 0.000262 0.000262\n", "102688 Southern M 1 1923 1923 0.000547 0.000547\n", "102689 Jeon M 1 1999 1999 0.000261 0.000261\n", "\n", "Tail of dataframe 'years':\n", " year births_f births_m births_t new_names unique_names_x sexratio \\\n", "68 2008 1886765 2035811 3922576 2046 32483 107.899553 \n", "69 2009 1832276 1978582 3810858 1789 32210 107.984932 \n", "70 2010 1771846 1912915 3684761 1635 31593 107.961696 \n", "71 2011 1752198 1891800 3643998 1539 31412 107.967250 \n", "72 2012 1751866 1886972 3638838 1531 31212 107.712120 \n", "\n", " unique_names_y_x unique_names_x unique_names_y_x unique_names_x \\\n", "68 32483 32483 32483 32483 \n", "69 32210 32210 32210 32210 \n", "70 31593 31593 31593 31593 \n", "71 31412 31412 31412 31412 \n", "72 31212 31212 31212 31212 \n", "\n", " unique_names_y_x unique_names_x unique_names_y_x unique_names_x \\\n", "68 32483 32483 32483 32483 \n", "69 32210 32210 32210 32210 \n", "70 31593 31593 31593 31593 \n", "71 31412 31412 31412 31412 \n", "72 31212 31212 31212 31212 \n", "\n", " unique_names_y_x unique_names_x unique_names_y_x unique_names \n", "68 32483 32483 32483 32483 \n", "69 32210 32210 32210 32210 \n", "70 31593 31593 31593 31593 \n", "71 31412 31412 31412 31412 \n", "72 31212 31212 31212 31212 \n", "--------------------\n", "Dataframe yob filtered to those that match list (count only)" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "1792091 records to begin.\n", "5045 records remaining." ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "--------------------\n", "Head of total matching list per year, female\n", " births pct\n", "year \n", "1880 83 0.091216\n", "1881 73 0.079388\n", "1882 77 0.071395\n", "1883 71 0.063211\n", "1884 106 0.082157\n", "\n", "\n" ] }, { "metadata": {}, "output_type": "display_data", "png": 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Sh8svXUI5l6O0tcmtJEmSJKlDdS1dQmmLLaG7e9D7mtxKkiRJkkZeqUT+vqVD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DNYl32M+vdf+XdSj4eY0SPRrVYyRjybf+cR2dq9W2XhQgvVqoXp0iUY6XAKBUluhRCikMl4+jm8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\n43awQgWNO+4IcscdIWrUMK4fi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"text": [ "" ] } ], "prompt_number": 14 }, { "cell_type": "code", "collapsed": false, "input": [ "# all names_listed, so we can see which ones to aggregate\n", "# cutoff of 10 already done\n", "\n", "print names_listed[names_listed.sex == 'M'].head(50)\n", "print ''\n", "print names_listed[names_listed.sex == 'F'].head(50)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ " name sex year_count year_min year_max pct_sum pct_max\n", "71603 Phoenix M 30 1968 2013 0.467450 0.041866\n", "66819 Odin M 70 1884 2013 0.181126 0.023885\n", "75344 Ares M 19 1983 2013 0.059744 0.012183\n", "68771 Apollo M 45 1965 2013 0.092614 0.010901\n", "65453 Thor M 103 1904 2013 0.223267 0.005603\n", "69242 Osiris M 42 1970 2013 0.067574 0.005343\n", "76827 Loki M 15 1996 2013 0.030797 0.004595\n", "70915 Zeus M 33 1973 2013 0.043210 0.004542\n", "65996 Hercules M 92 1908 2013 0.089908 0.003887\n", "73252 Mars M 24 1923 2013 0.012551 0.001431\n", "82133 Helios M 8 2000 2013 0.003586 0.001069\n", "68592 Hermes M 46 1924 2013 0.024318 0.000937\n", "86346 Poseidon M 4 2010 2013 0.002543 0.000802\n", "78294 Mercury M 13 1972 2012 0.004748 0.000705\n", "79245 Tyr M 11 2002 2013 0.005111 0.000695\n", "81705 Anubis M 8 2002 2012 0.002825 0.000516\n", "79701 Ra M 11 1972 2013 0.003533 0.000510\n", "79294 Jupiter M 11 1981 2013 0.003643 0.000455\n", "98214 Aten M 1 2013 2013 0.000267 0.000267\n", "91950 Fenris M 2 2011 2012 0.000529 0.000265\n", "95454 Horus M 1 2011 2011 0.000264 0.000264\n", "\n", " name sex year_count year_min year_max pct_sum pct_max\n", "1307 Athena F 104 1902 2013 1.335460 0.083207\n", "75 Minerva F 134 1880 2013 2.093317 0.069236\n", "679 Vesta F 127 1880 2012 1.481232 0.044859\n", "1049 Thalia F 112 1885 2013 0.696109 0.038711\n", "5300 Isis F 51 1901 2013 0.578503 0.030404\n", "2831 Eris F 74 1913 2013 0.105284 0.018735\n", "676 Venus F 127 1887 2013 0.610904 0.015362\n", "5884 Persephone F 48 1962 2013 0.077109 0.009616\n", "18907 Freyja F 17 1994 2013 0.020269 0.004376\n", "11122 Gaia F 29 1980 2013 0.038319 0.004224\n", "3353 Artemis F 68 1915 2013 0.045047 0.003800\n", "20819 Juno F 15 1919 2013 0.019627 0.003481\n", "7054 Lamia F 42 1968 2013 0.059279 0.002726\n", "5469 Clio F 50 1894 2013 0.047900 0.002709\n", "10392 Urania F 31 1891 2002 0.016488 0.002696\n", "6648 Andromeda F 44 1962 2013 0.034651 0.002303\n", "9510 Hera F 34 1970 2013 0.019592 0.001785\n", "20011 Valkyrie F 16 1992 2013 0.010817 0.001484\n", "14786 Athene F 22 1909 2003 0.011416 0.001440\n", "3992 Aphrodite F 61 1915 2013 0.036654 0.001409\n", "22754 Cybele F 13 1963 2010 0.008608 0.000948\n", "30199 Caliope F 7 1919 2013 0.003439 0.000916\n", "20116 Ourania F 15 1963 2013 0.006050 0.000819\n", "33277 Chimera F 6 1980 2001 0.002414 0.000659\n", "42757 Ceres F 3 2005 2013 0.001351 0.000633\n", "18008 Nike F 18 1953 2013 0.006075 0.000514\n", "21706 Vali F 14 1952 1967 0.005284 0.000512\n", "29183 Pallas F 8 1969 2007 0.003137 0.000508\n", "29550 Maat F 8 1998 2013 0.002521 0.000403\n", "58689 Tyche F 1 1999 1999 0.000282 0.000282\n", "62227 Khepri F 1 2009 2009 0.000273 0.000273\n" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "# just take top 10\n", "\n", "nice_round_number = 10 # if too high, there will be no change\n", "final_m = final_m[:nice_round_number]\n", "final_f = final_f[:nice_round_number]\n", "\n", "print 'After manually resizing to nice round number of %d names each:' % (nice_round_number)\n", "print 'Accepted male names:', final_m\n", "print 'Accepted female names:', final_f" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "After manually resizing to nice round number of 10 names each:\n", "Accepted male names: ['Thor', 'Hercules', 'Odin', 'Hermes', 'Apollo', 'Osiris', 'Zeus', 'Phoenix', 'Mars', 'Ares']\n", "Accepted female names: ['Athena', 'Minerva', 'Venus', 'Vesta', 'Thalia', 'Eris', 'Artemis', 'Aphrodite', 'Isis', 'Clio']\n" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [ "names = final_m[:10]\n", "sexes = ['M'] # can be length 1 or same length as names\n", "\n", "yearstart=1940\n", "yearend=2013\n", "\n", "start = time.time()\n", "df_chart = yob.copy()\n", "if len(sexes) == 1:\n", " sexes = sexes * len(names)\n", " \n", "df_chart = df_chart[df_chart['name'].isin(names)] \n", "\n", "df_chart['temp'] = 0\n", "for row in range(len(df_chart)):\n", " for pos in range(len(names)):\n", " if df_chart.name.iloc[row] == names[pos] and df_chart.sex.iloc[row] == sexes[pos]:\n", " df_chart.temp.iloc[row] = 1\n", "df_chart = df_chart[df_chart.temp == 1]\n", "\n", "\n", "#To keep more than one data set for charts in memory, change name of chart_1\n", "\n", "chart_1 = pd.DataFrame(pd.pivot_table(df_chart, values='pct', index = 'year', columns=['name', 'sex']))\n", "\n", "col = chart_1.columns[0]\n", "\n", "for yr in range(yearstart, yearend+1): #inserts missing years\n", " if yr not in chart_1.index:\n", " #chart_1[col][yr] = 0.0\n", " chart_1 = chart_1.append(pd.DataFrame(index=[yr], columns=[col], data=[0.0]))\n", "\n", "chart_1 = chart_1.fillna(0)\n", "\n", "chart_1.sort(inplace=True, ascending=True)\n", "\n", "#a single function to make the four different kinds of charts\n", "\n", "def make_chart(df=chart_1, form='line', title='', colors= [], smoothing=0, \\\n", " groupedlist = [], baseline='sym', png_name=''):\n", " \n", " dataframe = df.copy()\n", " \n", " startyear = min(list(dataframe.index))\n", " endyear = max(list(dataframe.index))\n", " yearstr = '%d-%d' % (startyear, endyear)\n", " \n", " legend_size = 0.01\n", " \n", " has_male = False\n", " has_female = False\n", " has_both = False\n", " max_y = 0\n", " for name, sex in dataframe.columns:\n", " max_y = max(max_y, dataframe[(name, sex)].max())\n", " final_name = name\n", " if sex == 'M': has_male = True\n", " if sex == 'F': has_female = True\n", " if smoothing > 0:\n", " newvalues = []\n", " for row in range(len(dataframe)):\n", " start = max(0, row - smoothing)\n", " end = min(len(dataframe) - 1, row + smoothing)\n", " newvalues.append(dataframe[(name, sex)].iloc[start:end].mean())\n", " for row in range(len(dataframe)):\n", " dataframe[(name, sex)].iloc[row] = newvalues[row]\n", " if has_male and has_female:\n", " y_text = \"% of births of indicated sex\"\n", " has_both = True\n", " elif has_male:\n", " y_text = \"Percent of male births\"\n", " else:\n", " y_text = \"Percent of female births\"\n", " \n", " num_series = len(dataframe.columns)\n", " \n", " if colors == []:\n", " colors = [\"#1f78b4\",\"#ae4ec9\",\"#33a02c\",\"#fb9a99\",\"#e31a1c\",\"#a6cee3\",\n", " \"#fdbf6f\",\"#ff7f00\",\"#cab2d6\",\"#6a3d9a\",\"#ffff99\",\"#b15928\"]\n", " #colors = ['#ff0000', '#b00000', '#870000', '#550000', '#e4e400', '#baba00', '#878700', '#545400', '#00ff00', '#00b000', '#008700', '#005500', '#00ffff', '#00b0b0', '#008787', '#005555', '#b0b0ff', '#8484ff', '#4949ff', '#0000ff', '#ff00ff', '#b000b0', '#870087', '#550055', '#e4e4e4', '#bababa', '#878787', '#545454']\n", " from random import shuffle\n", " shuffle(colors)\n", " num_colors = len(colors)\n", " \n", " if num_series > num_colors:\n", " print \"Warning: colors will be repeated.\"\n", " \n", " if title == '':\n", " if num_series == 1:\n", " title = \"Popularity of baby name %s in U.S., %s\" % (final_name, yearstr)\n", " else:\n", " title = \"Popularity of baby names in U.S., %s\" % (yearstr)\n", " \n", " x_values = range(startyear, endyear + 1)\n", " y_zeroes = [0] * (endyear - startyear)\n", " \n", " if form == 'line':\n", " fig, ax = plt.subplots(num=None, figsize=(16, 9), dpi=300, facecolor='w', edgecolor='w')\n", " counter = 0\n", " for name, sex in dataframe.columns:\n", " color = colors[counter % num_colors]\n", " counter += 1\n", " if has_both:\n", " label = \"%s (%s)\" % (name, sex)\n", " else:\n", " label = name\n", " ax.plot(x_values, dataframe[(name, sex)], label=label, color=color, linewidth = 3)\n", " ax.set_ylim(0,determine_y_limit(max_y)) \n", " ax.set_xlim(1980, endyear)\n", " ax.set_ylabel(y_text, size = 13)\n", " box = ax.get_position()\n", " ax.set_position([box.x0, box.y0 + box.height * legend_size,\n", " box.width, box.height * (1 - legend_size)])\n", " legend_cols = min(5, num_series)\n", " ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=legend_cols)\n", "\n", " if form == 'subplots_auto':\n", " counter = 0\n", " fig, axes = plt.subplots(num_series, 1, figsize=(12, 3.5*num_series))\n", " print 'Maximum alpha: %d percent' % (determine_y_limit(max_y))\n", " for name, sex in dataframe.columns:\n", " if sex=='M':\n", " sex_label = 'male'\n", " else:\n", " sex_label = 'female'\n", " label = \"Percent of %s births for %s\" % (sex_label, name)\n", " current_ymax = dataframe[(name, sex)].max()\n", " tint = 1.0 * current_ymax / determine_y_limit(max_y)\n", " axes[counter].plot(x_values, dataframe[(name, sex)], color='k')\n", " axes[counter].set_ylim(0,determine_y_limit(current_ymax))\n", " axes[counter].set_xlim(startyear, endyear)\n", " axes[counter].fill_between(x_values, dataframe[(name, sex)], color=colors[0], alpha=tint, interpolate=True)\n", "\n", " axes[counter].set_ylabel(label, size=11)\n", " plt.subplots_adjust(hspace=0.1)\n", " counter += 1\n", " \n", " if form == 'subplots_same':\n", " counter = 0\n", " fig, axes = plt.subplots(num_series, 1, figsize=(12, 3.5*num_series))\n", " print 'Maximum y axis: %d percent' % (determine_y_limit(max_y))\n", " for name, sex in dataframe.columns:\n", " if sex=='M':\n", " sex_label = 'male'\n", " else:\n", " sex_label = 'female'\n", " label = \"Percent of %s births for %s\" % (sex_label, name)\n", " axes[counter].plot(x_values, dataframe[(name, sex)], color='k')\n", " axes[counter].set_ylim(0,determine_y_limit(max_y))\n", " axes[counter].set_xlim(startyear, endyear)\n", " axes[counter].fill_between(x_values, dataframe[(name, sex)], color=colors[1], alpha=1, interpolate=True)\n", " axes[counter].set_ylabel(label, size=11)\n", " plt.subplots_adjust(hspace=0.1)\n", " counter += 1\n", " \n", " if form == 'stream':\n", " plt.figure(num=None, figsize=(20,10), dpi=150, facecolor='w', edgecolor='k')\n", " plt.title(title, size=17) \n", " plt.xlim(startyear, endyear)\n", " \n", " if has_both:\n", " yaxtext = 'Percent of births of indicated sex (scale: '\n", " elif has_male:\n", " yaxtext = 'Percent of male births (scale: '\n", " else:\n", " yaxtext = 'Percent of female births (scale: '\n", " \n", " scale = str(determine_y_limit(max_y)) + ')'\n", " yaxtext += scale\n", " plt.ylabel(yaxtext, size=13)\n", " polys = plt.stackplot(x_values, *[dataframe[(name, sex)] for name, sex in dataframe.columns], \n", " colors=colors, baseline=baseline)\n", " legendProxies = []\n", " for poly in polys:\n", " legendProxies.append(plt.Rectangle((0, 0), 1, 1, fc=poly.get_facecolor()[0]))\n", " namelist = []\n", " for name, sex in dataframe.columns:\n", " if has_both:\n", " namelist.append('%s (%s)' % (name, sex))\n", " else:\n", " namelist.append(name)\n", " plt.legend(legendProxies, namelist, loc=3, ncol=2)\n", " \n", " plt.tick_params(\\\n", " axis='y', \n", " which='both', # major and minor ticks \n", " left='off', \n", " right='off', \n", " labelleft='off')\n", " \n", " plt.show() \n", " if png_name != '':\n", " filename = save_path + \"/\" + png_name + \".png\"\n", " plt.savefig(filename)\n", " plt.close()\n", " \n", "#line graph\n", "\n", "make_chart(df=chart_1,\n", " form='stream', # line , subplots_auto , subplots_same , stream\n", " title='',\n", " colors= [],\n", " smoothing=0,\n", " baseline='sym', # zero , sym , wiggle , weighted_wiggle\n", " png_name = '', # if '', will not be saved\n", " )" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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gAAAAAADIeVaNHqkOh/+UHlq+dcXBQdfLhKlb+ch0j7vg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\nJ5ur5w7E4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FcOLUbXDrH9QAOffdxEUb0GFidGuwuzIwaz3YnN6SKpTzZpuTNwxSciSRI+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\nAAAAAIDare1F3TR/z27F5eUr1N+v1D2Lx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"text": [ "" ] } ], "prompt_number": 17 }, { "cell_type": "code", "collapsed": false, "input": [ "names = final_f[:12]\n", "sexes = ['F'] # can be length 1 or same length as names\n", "\n", "yearstart=1940\n", "yearend=2013\n", "\n", "start = time.time()\n", "df_chart = yob.copy()\n", "if len(sexes) == 1:\n", " sexes = sexes * len(names)\n", " \n", "df_chart = df_chart[df_chart['name'].isin(names)] \n", "\n", "df_chart['temp'] = 0\n", "for row in range(len(df_chart)):\n", " for pos in range(len(names)):\n", " if df_chart.name.iloc[row] == names[pos] and df_chart.sex.iloc[row] == sexes[pos]:\n", " df_chart.temp.iloc[row] = 1\n", "df_chart = df_chart[df_chart.temp == 1]\n", "\n", "\n", "#To keep more than one data set for charts in memory, change name of chart_1\n", "\n", "chart_1 = pd.DataFrame(pd.pivot_table(df_chart, values='pct', index = 'year', columns=['name', 'sex']))\n", "\n", "col = chart_1.columns[0]\n", "\n", "for yr in range(yearstart, yearend+1): #inserts missing years\n", " if yr not in chart_1.index:\n", " #chart_1[col][yr] = 0.0\n", " chart_1 = chart_1.append(pd.DataFrame(index=[yr], columns=[col], data=[0.0]))\n", "\n", "chart_1 = chart_1.fillna(0)\n", "\n", "chart_1.sort(inplace=True, ascending=True)\n", "\n", "#a single function to make the four different kinds of charts\n", "\n", "def make_chart(df=chart_1, form='line', title='', colors= [], smoothing=0, \\\n", " groupedlist = [], baseline='sym', png_name=''):\n", " \n", " dataframe = df.copy()\n", " \n", " startyear = min(list(dataframe.index))\n", " endyear = max(list(dataframe.index))\n", " yearstr = '%d-%d' % (startyear, endyear)\n", " \n", " legend_size = 0.01\n", " \n", " has_male = False\n", " has_female = False\n", " has_both = False\n", " max_y = 0\n", " for name, sex in dataframe.columns:\n", " max_y = max(max_y, dataframe[(name, sex)].max())\n", " final_name = name\n", " if sex == 'M': has_male = True\n", " if sex == 'F': has_female = True\n", " if smoothing > 0:\n", " newvalues = []\n", " for row in range(len(dataframe)):\n", " start = max(0, row - smoothing)\n", " end = min(len(dataframe) - 1, row + smoothing)\n", " newvalues.append(dataframe[(name, sex)].iloc[start:end].mean())\n", " for row in range(len(dataframe)):\n", " dataframe[(name, sex)].iloc[row] = newvalues[row]\n", " if has_male and has_female:\n", " y_text = \"% of births of indicated sex\"\n", " has_both = True\n", " elif has_male:\n", " y_text = \"Percent of male births\"\n", " else:\n", " y_text = \"Percent of female births\"\n", " \n", " num_series = len(dataframe.columns)\n", " \n", " if colors == []:\n", " colors = [\"#1f78b4\",\"#ae4ec9\",\"#33a02c\",\"#fb9a99\",\"#e31a1c\",\"#a6cee3\",\n", " \"#fdbf6f\",\"#ff7f00\",\"#cab2d6\",\"#6a3d9a\",\"#ffff99\",\"#b15928\"]\n", " #colors = ['#ff0000', '#b00000', '#870000', '#550000', '#e4e400', '#baba00', '#878700', '#545400', '#00ff00', '#00b000', '#008700', '#005500', '#00ffff', '#00b0b0', '#008787', '#005555', '#b0b0ff', '#8484ff', '#4949ff', '#0000ff', '#ff00ff', '#b000b0', '#870087', '#550055', '#e4e4e4', '#bababa', '#878787', '#545454']\n", " from random import shuffle\n", " shuffle(colors)\n", " num_colors = len(colors)\n", " \n", " if num_series > num_colors:\n", " print \"Warning: colors will be repeated.\"\n", " \n", " if title == '':\n", " if num_series == 1:\n", " title = \"Popularity of baby name %s in U.S., %s\" % (final_name, yearstr)\n", " else:\n", " title = \"Popularity of baby names in U.S., %s\" % (yearstr)\n", " \n", " x_values = range(startyear, endyear + 1)\n", " y_zeroes = [0] * (endyear - startyear)\n", " \n", " if form == 'line':\n", " fig, ax = plt.subplots(num=None, figsize=(16, 9), dpi=300, facecolor='w', edgecolor='w')\n", " counter = 0\n", " for name, sex in dataframe.columns:\n", " color = colors[counter % num_colors]\n", " counter += 1\n", " if has_both:\n", " label = \"%s (%s)\" % (name, sex)\n", " else:\n", " label = name\n", " ax.plot(x_values, dataframe[(name, sex)], label=label, color=color, linewidth = 3)\n", " ax.set_ylim(0,determine_y_limit(max_y)) \n", " ax.set_xlim(1980, endyear)\n", " ax.set_ylabel(y_text, size = 13)\n", " box = ax.get_position()\n", " ax.set_position([box.x0, box.y0 + box.height * legend_size,\n", " box.width, box.height * (1 - legend_size)])\n", " legend_cols = min(5, num_series)\n", " ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=legend_cols)\n", "\n", " if form == 'subplots_auto':\n", " counter = 0\n", " fig, axes = plt.subplots(num_series, 1, figsize=(12, 3.5*num_series))\n", " print 'Maximum alpha: %d percent' % (determine_y_limit(max_y))\n", " for name, sex in dataframe.columns:\n", " if sex=='M':\n", " sex_label = 'male'\n", " else:\n", " sex_label = 'female'\n", " label = \"Percent of %s births for %s\" % (sex_label, name)\n", " current_ymax = dataframe[(name, sex)].max()\n", " tint = 1.0 * current_ymax / determine_y_limit(max_y)\n", " axes[counter].plot(x_values, dataframe[(name, sex)], color='k')\n", " axes[counter].set_ylim(0,determine_y_limit(current_ymax))\n", " axes[counter].set_xlim(startyear, endyear)\n", " axes[counter].fill_between(x_values, dataframe[(name, sex)], color=colors[0], alpha=tint, interpolate=True)\n", "\n", " axes[counter].set_ylabel(label, size=11)\n", " plt.subplots_adjust(hspace=0.1)\n", " counter += 1\n", " \n", " if form == 'subplots_same':\n", " counter = 0\n", " fig, axes = plt.subplots(num_series, 1, figsize=(12, 3.5*num_series))\n", " print 'Maximum y axis: %d percent' % (determine_y_limit(max_y))\n", " for name, sex in dataframe.columns:\n", " if sex=='M':\n", " sex_label = 'male'\n", " else:\n", " sex_label = 'female'\n", " label = \"Percent of %s births for %s\" % (sex_label, name)\n", " axes[counter].plot(x_values, dataframe[(name, sex)], color='k')\n", " axes[counter].set_ylim(0,determine_y_limit(max_y))\n", " axes[counter].set_xlim(startyear, endyear)\n", " axes[counter].fill_between(x_values, dataframe[(name, sex)], color=colors[1], alpha=1, interpolate=True)\n", " axes[counter].set_ylabel(label, size=11)\n", " plt.subplots_adjust(hspace=0.1)\n", " counter += 1\n", " \n", " if form == 'stream':\n", " plt.figure(num=None, figsize=(20,10), dpi=150, facecolor='w', edgecolor='k')\n", " plt.title(title, size=17) \n", " plt.xlim(startyear, endyear)\n", " \n", " if has_both:\n", " yaxtext = 'Percent of births of indicated sex (scale: '\n", " elif has_male:\n", " yaxtext = 'Percent of male births (scale: '\n", " else:\n", " yaxtext = 'Percent of female births (scale: '\n", " \n", " scale = str(determine_y_limit(max_y)) + ')'\n", " yaxtext += scale\n", " plt.ylabel(yaxtext, size=13)\n", " polys = plt.stackplot(x_values, *[dataframe[(name, sex)] for name, sex in dataframe.columns], \n", " colors=colors, baseline=baseline)\n", " legendProxies = []\n", " for poly in polys:\n", " legendProxies.append(plt.Rectangle((0, 0), 1, 1, fc=poly.get_facecolor()[0]))\n", " namelist = []\n", " for name, sex in dataframe.columns:\n", " if has_both:\n", " namelist.append('%s (%s)' % (name, sex))\n", " else:\n", " namelist.append(name)\n", " plt.legend(legendProxies, namelist, loc=3, ncol=2)\n", " \n", " plt.tick_params(\\\n", " axis='y', \n", " which='both', # major and minor ticks \n", " left='off', \n", " right='off', \n", " labelleft='off')\n", " \n", " plt.show() \n", " if png_name != '':\n", " filename = save_path + \"/\" + png_name + \".png\"\n", " plt.savefig(filename)\n", " plt.close()\n", " \n", "#line graph\n", "\n", "make_chart(df=chart_1,\n", " form='stream', # line , subplots_auto , subplots_same , stream\n", " title=\"10 most popular mythological girls' names, 2914-2013\",\n", " colors= [],\n", " smoothing=0,\n", " baseline='sym', # zero , sym , wiggle , weighted_wiggle\n", " png_name = '', # if '', will not be saved\n", " )" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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QEREREREREb1EGPYQEREREREREb1EGPYQEREREREREb1EGPYQEREREREREb1E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JAAAg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\ngqcsn0JbOpe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av42UUKthU6meV7YYidLcypSYC7p9fFEUeSfrASL908i2yZw1cQj3XpaK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\nnXHP86FBVjPGuEy/vJLl1p5UNZvkylgibE7x4BNzcbBz4Ez4SXIvneKT/j2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IW5ZTqCYs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ULP3xwbO3KqhgWVvxkMCvcu3kXHeSuY8d5uFv9+mkOhaWh1Vz93umRLGF+ci8R7nh+VaVq8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\nI6k+xZmpPsWZqT7Fmak+xZmpPp3HD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"text": [ "" ] } ], "prompt_number": 18 }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Values before WWII are HEAVILY waited towards white middle-class, so refine these graphs to show 1945-present" ] }, { "cell_type": "code", "collapsed": false, "input": [ "names = final_f[:10]\n", "sexes = ['F'] # can be length 1 or same length as names\n", "\n", "yearstart=1940 # for data, not graph\n", "yearend=2013\n", "\n", "xmin = 1940\n", "\n", "start = time.time()\n", "df_chart = yob.copy()\n", "if len(sexes) == 1:\n", " sexes = sexes * len(names)\n", " \n", "df_chart = df_chart[df_chart['name'].isin(names)] \n", "\n", "df_chart['temp'] = 0\n", "for row in range(len(df_chart)):\n", " for pos in range(len(names)):\n", " if df_chart.name.iloc[row] == names[pos] and df_chart.sex.iloc[row] == sexes[pos]:\n", " df_chart.temp.iloc[row] = 1\n", "df_chart = df_chart[df_chart.temp == 1]\n", "\n", "\n", "#To keep more than one data set for charts in memory, change name of chart_1\n", "\n", "chart_1 = pd.DataFrame(pd.pivot_table(df_chart, values='pct', index = 'year', columns=['name', 'sex']))\n", "\n", "col = chart_1.columns[0]\n", "\n", "for yr in range(yearstart, yearend+1): #inserts missing years\n", " if yr not in chart_1.index:\n", " #chart_1[col][yr] = 0.0\n", " chart_1 = chart_1.append(pd.DataFrame(index=[yr], columns=[col], data=[0.0]))\n", "\n", "chart_1 = chart_1.fillna(0)\n", "\n", "chart_1.sort(inplace=True, ascending=True)\n", "\n", "#a single function to make the four different kinds of charts\n", "\n", "def make_chart(df=chart_1, form='line', title='', colors= [], smoothing=0, \\\n", " groupedlist = [], baseline='sym', png_name=''):\n", " \n", " dataframe = df.copy()\n", " \n", " startyear = min(list(dataframe.index))\n", " endyear = max(list(dataframe.index))\n", " yearstr = '%d-%d' % (startyear, endyear)\n", " \n", " legend_size = 0.01\n", " \n", " has_male = False\n", " has_female = False\n", " has_both = False\n", " max_y = 0\n", " for name, sex in dataframe.columns:\n", " max_y = max(max_y, dataframe[(name, sex)].max())\n", " final_name = name\n", " if sex == 'M': has_male = True\n", " if sex == 'F': has_female = True\n", " if smoothing > 0:\n", " newvalues = []\n", " for row in range(len(dataframe)):\n", " start = max(0, row - smoothing)\n", " end = min(len(dataframe) - 1, row + smoothing)\n", " newvalues.append(dataframe[(name, sex)].iloc[start:end].mean())\n", " for row in range(len(dataframe)):\n", " dataframe[(name, sex)].iloc[row] = newvalues[row]\n", " if has_male and has_female:\n", " y_text = \"% of births of indicated sex\"\n", " has_both = True\n", " elif has_male:\n", " y_text = \"Percent of male births\"\n", " else:\n", " y_text = \"Percent of female births\"\n", " \n", " num_series = len(dataframe.columns)\n", " \n", " if colors == []:\n", " colors = [\"#1f78b4\",\"#ae4ec9\",\"#33a02c\",\"#fb9a99\",\"#e31a1c\",\"#a6cee3\",\n", " \"#fdbf6f\",\"#ff7f00\",\"#cab2d6\",\"#6a3d9a\",\"#ffff99\",\"#b15928\"]\n", " #colors = ['#ff0000', '#b00000', '#870000', '#550000', '#e4e400', '#baba00', '#878700', '#545400', '#00ff00', '#00b000', '#008700', '#005500', '#00ffff', '#00b0b0', '#008787', '#005555', '#b0b0ff', '#8484ff', '#4949ff', '#0000ff', '#ff00ff', '#b000b0', '#870087', '#550055', '#e4e4e4', '#bababa', '#878787', '#545454']\n", " from random import shuffle\n", " shuffle(colors)\n", " num_colors = len(colors)\n", " \n", " if num_series > num_colors:\n", " print \"Warning: colors will be repeated.\"\n", " \n", " if title == '':\n", " if num_series == 1:\n", " title = \"Popularity of baby name %s in U.S., %s\" % (final_name, yearstr)\n", " else:\n", " title = \"Popularity of baby names in U.S., %s\" % (yearstr)\n", " \n", " x_values = range(startyear, endyear + 1)\n", " y_zeroes = [0] * (endyear - startyear)\n", " \n", " if form == 'line':\n", " fig, ax = plt.subplots(num=None, figsize=(16, 9), dpi=300, facecolor='w', edgecolor='w')\n", " counter = 0\n", " for name, sex in dataframe.columns:\n", " color = colors[counter % num_colors]\n", " counter += 1\n", " if has_both:\n", " label = \"%s (%s)\" % (name, sex)\n", " else:\n", " label = name\n", " ax.plot(x_values, dataframe[(name, sex)], label=label, color=color, linewidth = 3)\n", " ax.set_ylim(0,determine_y_limit(max_y)) \n", " ax.set_xlim(xmin, endyear)\n", " ax.set_ylabel(y_text, size = 13)\n", " box = ax.get_position()\n", " ax.set_position([box.x0, box.y0 + box.height * legend_size,\n", " box.width, box.height * (1 - legend_size)])\n", " legend_cols = min(5, num_series)\n", " ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=legend_cols)\n", "\n", " if form == 'subplots_auto':\n", " counter = 0\n", " fig, axes = plt.subplots(num_series, 1, figsize=(12, 3.5*num_series))\n", " print 'Maximum alpha: %d percent' % (determine_y_limit(max_y))\n", " for name, sex in dataframe.columns:\n", " if sex=='M':\n", " sex_label = 'male'\n", " else:\n", " sex_label = 'female'\n", " label = \"Percent of %s births for %s\" % (sex_label, name)\n", " current_ymax = dataframe[(name, sex)].max()\n", " tint = 1.0 * current_ymax / determine_y_limit(max_y)\n", " axes[counter].plot(x_values, dataframe[(name, sex)], color='k')\n", " axes[counter].set_ylim(0,determine_y_limit(current_ymax))\n", " axes[counter].set_xlim(xmin, endyear)\n", " axes[counter].fill_between(x_values, dataframe[(name, sex)], color=colors[0], alpha=tint, interpolate=True)\n", "\n", " axes[counter].set_ylabel(label, size=11)\n", " plt.subplots_adjust(hspace=0.1)\n", " counter += 1\n", " \n", " if form == 'subplots_same':\n", " counter = 0\n", " fig, axes = plt.subplots(num_series, 1, figsize=(12, 3.5*num_series))\n", " print 'Maximum y axis: %d percent' % (determine_y_limit(max_y))\n", " for name, sex in dataframe.columns:\n", " if sex=='M':\n", " sex_label = 'male'\n", " else:\n", " sex_label = 'female'\n", " label = \"Percent of %s births for %s\" % (sex_label, name)\n", " axes[counter].plot(x_values, dataframe[(name, sex)], color='k')\n", " axes[counter].set_ylim(0,determine_y_limit(max_y))\n", " axes[counter].set_xlim(xmin, endyear)\n", " axes[counter].fill_between(x_values, dataframe[(name, sex)], color=colors[1], alpha=1, interpolate=True)\n", " axes[counter].set_ylabel(label, size=11)\n", " plt.subplots_adjust(hspace=0.1)\n", " counter += 1\n", " \n", " if form == 'stream':\n", " plt.figure(num=None, figsize=(20,16.67), dpi=150, facecolor='w', edgecolor='k')\n", " plt.title(title, size=17) \n", " plt.xlim(xmin, endyear)\n", " \n", " if has_both:\n", " yaxtext = 'Percent of births of indicated sex (scale: '\n", " elif has_male:\n", " yaxtext = 'Percent of male births (scale: '\n", " else:\n", " yaxtext = 'Percent of female births (scale: '\n", " \n", " scale = str(determine_y_limit(max_y)) + ')'\n", " yaxtext += scale\n", " plt.ylabel(yaxtext, size=13)\n", " polys = plt.stackplot(x_values, *[dataframe[(name, sex)] for name, sex in dataframe.columns], \n", " colors=colors, baseline=baseline)\n", " legendProxies = []\n", " for poly in polys:\n", " legendProxies.append(plt.Rectangle((0, 0), 1, 1, fc=poly.get_facecolor()[0]))\n", " namelist = []\n", " for name, sex in dataframe.columns:\n", " if has_both:\n", " namelist.append('%s (%s)' % (name, sex))\n", " else:\n", " namelist.append(name)\n", " plt.legend(legendProxies, namelist, loc=3, ncol=2)\n", " \n", " plt.tick_params(\\\n", " axis='y', \n", " which='both', # major and minor ticks \n", " left='off', \n", " right='off', \n", " labelleft='off')\n", " \n", " plt.show() \n", " if png_name != '':\n", " filename = save_path + \"/\" + png_name + \".png\"\n", " plt.savefig(filename)\n", " plt.close()\n", " \n", "#stream graph\n", "\n", "make_chart(df=chart_1,\n", " form='stream', # line , subplots_auto , subplots_same , stream\n", " title='',\n", " colors= [],\n", " smoothing=0,\n", " baseline='sym', # zero , sym , wiggle , weighted_wiggle\n", " png_name = '', # if '', will not be saved\n", " )" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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NMAztTdiu+i5dKrsVqzTnSJndC8YylsMOSmk06pYCG8Ie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FAAAg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\nPJJ8m/nwqbl9HVK3TB8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VaCJOxOKukiBdj7rGj5pyMzaffqDSYNu3HN/qNA78ewQedXwK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NRkIi6cTcaYcLbaHGMOh4Ofn59D52W/EGazn/LcckXjSIg3Zh8DHbwribGYCWzVnFtHhhHg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\n+9OPdaQ4Uda6DGUXd1e3mGBPxG+x/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/6H40wmE15e3qxY8U9cXFxI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"text": [ "" ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [ "names = final_m[:10]\n", "sexes = ['M'] # can be length 1 or same length as names\n", "\n", "yearstart=1940 # for data, not graph\n", "yearend=2013\n", "\n", "xmin = 1940\n", "\n", "start = time.time()\n", "df_chart = yob.copy()\n", "if len(sexes) == 1:\n", " sexes = sexes * len(names)\n", " \n", "df_chart = df_chart[df_chart['name'].isin(names)] \n", "\n", "df_chart['temp'] = 0\n", "for row in range(len(df_chart)):\n", " for pos in range(len(names)):\n", " if df_chart.name.iloc[row] == names[pos] and df_chart.sex.iloc[row] == sexes[pos]:\n", " df_chart.temp.iloc[row] = 1\n", "df_chart = df_chart[df_chart.temp == 1]\n", "\n", "\n", "#To keep more than one data set for charts in memory, change name of chart_1\n", "\n", "chart_1 = pd.DataFrame(pd.pivot_table(df_chart, values='pct', index = 'year', columns=['name', 'sex']))\n", "\n", "col = chart_1.columns[0]\n", "\n", "for yr in range(yearstart, yearend+1): #inserts missing years\n", " if yr not in chart_1.index:\n", " #chart_1[col][yr] = 0.0\n", " chart_1 = chart_1.append(pd.DataFrame(index=[yr], columns=[col], data=[0.0]))\n", "\n", "chart_1 = chart_1.fillna(0)\n", "\n", "chart_1.sort(inplace=True, ascending=True)\n", "\n", "#a single function to make the four different kinds of charts\n", "\n", "def make_chart(df=chart_1, form='line', title='', colors= [], smoothing=0, \\\n", " groupedlist = [], baseline='sym', png_name=''):\n", " \n", " dataframe = df.copy()\n", " \n", " startyear = min(list(dataframe.index))\n", " endyear = max(list(dataframe.index))\n", " yearstr = '%d-%d' % (startyear, endyear)\n", " \n", " legend_size = 0.01\n", " \n", " has_male = False\n", " has_female = False\n", " has_both = False\n", " max_y = 0\n", " for name, sex in dataframe.columns:\n", " max_y = max(max_y, dataframe[(name, sex)].max())\n", " final_name = name\n", " if sex == 'M': has_male = True\n", " if sex == 'F': has_female = True\n", " if smoothing > 0:\n", " newvalues = []\n", " for row in range(len(dataframe)):\n", " start = max(0, row - smoothing)\n", " end = min(len(dataframe) - 1, row + smoothing)\n", " newvalues.append(dataframe[(name, sex)].iloc[start:end].mean())\n", " for row in range(len(dataframe)):\n", " dataframe[(name, sex)].iloc[row] = newvalues[row]\n", " if has_male and has_female:\n", " y_text = \"% of births of indicated sex\"\n", " has_both = True\n", " elif has_male:\n", " y_text = \"Percent of male births\"\n", " else:\n", " y_text = \"Percent of female births\"\n", " \n", " num_series = len(dataframe.columns)\n", " \n", " if colors == []:\n", " colors = [\"#1f78b4\",\"#ae4ec9\",\"#33a02c\",\"#fb9a99\",\"#e31a1c\",\"#a6cee3\",\n", " \"#fdbf6f\",\"#ff7f00\",\"#cab2d6\",\"#6a3d9a\",\"#ffff99\",\"#b15928\"]\n", " #colors = ['#ff0000', '#b00000', '#870000', '#550000', '#e4e400', '#baba00', '#878700', '#545400', '#00ff00', '#00b000', '#008700', '#005500', '#00ffff', '#00b0b0', '#008787', '#005555', '#b0b0ff', '#8484ff', '#4949ff', '#0000ff', '#ff00ff', '#b000b0', '#870087', '#550055', '#e4e4e4', '#bababa', '#878787', '#545454']\n", " from random import shuffle\n", " shuffle(colors)\n", " num_colors = len(colors)\n", " \n", " if num_series > num_colors:\n", " print \"Warning: colors will be repeated.\"\n", " \n", " if title == '':\n", " if num_series == 1:\n", " title = \"Popularity of baby name %s in U.S., %s\" % (final_name, yearstr)\n", " else:\n", " title = \"Popularity of baby names in U.S., %s\" % (yearstr)\n", " \n", " x_values = range(startyear, endyear + 1)\n", " y_zeroes = [0] * (endyear - startyear)\n", " \n", " if form == 'line':\n", " fig, ax = plt.subplots(num=None, figsize=(16, 9), dpi=300, facecolor='w', edgecolor='w')\n", " counter = 0\n", " for name, sex in dataframe.columns:\n", " color = colors[counter % num_colors]\n", " counter += 1\n", " if has_both:\n", " label = \"%s (%s)\" % (name, sex)\n", " else:\n", " label = name\n", " ax.plot(x_values, dataframe[(name, sex)], label=label, color=color, linewidth = 3)\n", " ax.set_ylim(0,determine_y_limit(max_y)) \n", " ax.set_xlim(xmin, endyear)\n", " ax.set_ylabel(y_text, size = 13)\n", " box = ax.get_position()\n", " ax.set_position([box.x0, box.y0 + box.height * legend_size,\n", " box.width, box.height * (1 - legend_size)])\n", " legend_cols = min(5, num_series)\n", " ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), fancybox=True, shadow=True, ncol=legend_cols)\n", "\n", " if form == 'subplots_auto':\n", " counter = 0\n", " fig, axes = plt.subplots(num_series, 1, figsize=(12, 3.5*num_series))\n", " print 'Maximum alpha: %d percent' % (determine_y_limit(max_y))\n", " for name, sex in dataframe.columns:\n", " if sex=='M':\n", " sex_label = 'male'\n", " else:\n", " sex_label = 'female'\n", " label = \"Percent of %s births for %s\" % (sex_label, name)\n", " current_ymax = dataframe[(name, sex)].max()\n", " tint = 1.0 * current_ymax / determine_y_limit(max_y)\n", " axes[counter].plot(x_values, dataframe[(name, sex)], color='k')\n", " axes[counter].set_ylim(0,determine_y_limit(current_ymax))\n", " axes[counter].set_xlim(xmin, endyear)\n", " axes[counter].fill_between(x_values, dataframe[(name, sex)], color=colors[0], alpha=tint, interpolate=True)\n", "\n", " axes[counter].set_ylabel(label, size=11)\n", " plt.subplots_adjust(hspace=0.1)\n", " counter += 1\n", " \n", " if form == 'subplots_same':\n", " counter = 0\n", " fig, axes = plt.subplots(num_series, 1, figsize=(12, 3.5*num_series))\n", " print 'Maximum y axis: %d percent' % (determine_y_limit(max_y))\n", " for name, sex in dataframe.columns:\n", " if sex=='M':\n", " sex_label = 'male'\n", " else:\n", " sex_label = 'female'\n", " label = \"Percent of %s births for %s\" % (sex_label, name)\n", " axes[counter].plot(x_values, dataframe[(name, sex)], color='k')\n", " axes[counter].set_ylim(0,determine_y_limit(max_y))\n", " axes[counter].set_xlim(xmin, endyear)\n", " axes[counter].fill_between(x_values, dataframe[(name, sex)], color=colors[1], alpha=1, interpolate=True)\n", " axes[counter].set_ylabel(label, size=11)\n", " plt.subplots_adjust(hspace=0.1)\n", " counter += 1\n", " \n", " if form == 'stream':\n", " plt.figure(num=None, figsize=(20,10), dpi=150, facecolor='w', edgecolor='k')\n", " plt.title(title, size=17) \n", " plt.xlim(xmin, endyear)\n", " \n", " if has_both:\n", " yaxtext = 'Percent of births of indicated sex (scale: '\n", " elif has_male:\n", " yaxtext = 'Percent of male births (scale: '\n", " else:\n", " yaxtext = 'Percent of female births (scale: '\n", " \n", " scale = str(determine_y_limit(max_y)) + ')'\n", " yaxtext += scale\n", " plt.ylabel(yaxtext, size=13)\n", " polys = plt.stackplot(x_values, *[dataframe[(name, sex)] for name, sex in dataframe.columns], \n", " colors=colors, baseline=baseline)\n", " legendProxies = []\n", " for poly in polys:\n", " legendProxies.append(plt.Rectangle((0, 0), 1, 1, fc=poly.get_facecolor()[0]))\n", " namelist = []\n", " for name, sex in dataframe.columns:\n", " if has_both:\n", " namelist.append('%s (%s)' % (name, sex))\n", " else:\n", " namelist.append(name)\n", " plt.legend(legendProxies, namelist, loc=3, ncol=2)\n", " \n", " plt.tick_params(\\\n", " axis='y', \n", " which='both', # major and minor ticks \n", " left='off', \n", " right='off', \n", " labelleft='off')\n", " \n", " plt.show() \n", " if png_name != '':\n", " filename = save_path + \"/\" + png_name + \".png\"\n", " plt.savefig(filename)\n", " plt.close()\n", " \n", "#stream graph\n", "\n", "make_chart(df=chart_1,\n", " form='stream', # line , subplots_auto , subplots_same , stream\n", " title='',\n", " colors= [],\n", " smoothing=0,\n", " baseline='sym', # zero , sym , wiggle , weighted_wiggle\n", " png_name = '', # if '', will not be saved\n", " )" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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39cmmhzpftleG92RxvHNSEz8okX0F46mW7nyvUqVKafHi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\nDZfOvES+XmZGmmoJX7map+74kBefWc6hQ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8W36jAHt21w65ie1/yc76J/wcLSMI/2JS0E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\nWVlnPthgID/fvSFZMpmWUEUmaxOqScaEapIxoZpkTKgmGROqS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